diff --git a/README.md b/README.md index 6bc8889..6015ef5 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,78 @@ -# How To Run +# MCP-VectorSQL + +## Overview + +MCP-VectorSQL is a powerful vector SQL generation tool that converts natural language questions into high-quality SQL queries, specifically designed for vector databases. It enables users to interact with vector databases using natural language, simplifying complex vector search operations. + +## Architecture + +![Text2VectorSQL Evaluation Process](./benchmark/figures/mcp_vector_sql.png) + +The architecture consists of three main components: + +1. **Text2VectorSql**: Handles natural language input and generates unified SQL output +2. **LLM**: Processes natural language questions and generates vector queries +3. **VecDB (MyScale)**: Performs vector similarity searches and stores vector data + +The workflow includes: +- Step 1: LLM lists database tables and schemas from the vector database +- Step 2: Text2VectorSql gets vector queries based on natural language questions +- Step 3: VecDB executes vector queries and returns results + +## Core Features + +### Natural Language Processing +- Accepts direct natural language questions from users +- Converts natural language into structured vector queries +- Supports complex questions with multiple conditions + +### Vector Similarity Search +- Performs efficient similarity searches on vector databases +- Supports various similarity metrics (cosine similarity, Euclidean distance, etc.) +- Optimized for large-scale vector datasets + +### Answer Integration +- Processes and integrates results from vector searches +- Combines information from multiple sources if needed +- Generates coherent and comprehensive answers + +### Response Generation +- Returns natural language answers based on search results +- Provides relevant and accurate information to users +- Maintains context and relevance throughout the conversation + +## Quick Start + +### 1. Configure Environment -1. Configuare local service env: ```bash +# Copy environment variable example file cp .env.example .env ``` -Modify ref env config - +Modify the `.env` file with your configuration: +- API settings (API_KEY, API_URL, etc.) +- Database settings (MYSCALE_HOST, MYSCALE_PORT, MYSCALE_USER, etc.) +- Server settings (MCP_SERVER_TRANSPORT, MCP_BIND_HOST, etc.) -2. Run Mcp Server +### 2. Initialize and Run MCP Server ```bash -# init runtime env +# Initialize runtime environment uv sync --all-extras --dev -# run mcp server +# Run MCP server uv run python -m mcp_server.main ``` -3. Regist Mcp Tools in Dify +### 3. Register MCP Tools in Dify + +Register the MCP server with the Dify platform to use its SQL generation capabilities. + +## License + +Please refer to the [LICENSE](LICENSE) file for license information. + +## Contact + +For questions or suggestions, please contact the development team. diff --git a/benchmark/.env.example b/benchmark/.env.example new file mode 100644 index 0000000..4101004 --- /dev/null +++ b/benchmark/.env.example @@ -0,0 +1,16 @@ +# Dify API +API_KEY=your-api-key-here +DIFY_URL=https://api.dify.ai/v1/chat-messages + +# MyScale config or other vector database configuration +MYSCALE_HOST=your-myscale-host-here +MYSCALE_PORT=8123 +MYSCALE_USER=your-myscale-username-here +MYSCALE_PASSWORD=your-myscale-password-here +MYSCALE_DATABASE=your-database-name-here + +# LLM API +LLM_API_URL=https://your-llm-api-url-here/v1/chat/completions +LLM_API_KEY=your-llm-api-key-here +LLM_MODEL=your-llm-model-name-here +LLM_EVALUATION_ENABLED=True \ No newline at end of file diff --git a/benchmark/READMD.md b/benchmark/READMD.md new file mode 100644 index 0000000..24c2141 --- /dev/null +++ b/benchmark/READMD.md @@ -0,0 +1,167 @@ +# MCP SQLVectorDB Benchmark + +This benchmark is designed to evaluate the performance of MCP SQLVectorDB models on Text2VectorSQL tasks. It provides comprehensive metrics to assess the accuracy, recall, and overall quality of SQL generation from natural language questions. + +## How to Run this Benchmark + +### Prerequisites + +Before running the benchmark, ensure you have: +- Python 3.8+ +- Dify API access with a valid API key +- LLM API access with a valid API key +- MyScale database access +- Required Python packages (install via `pip install -r requirements.txt`) + +### Configuration + +The benchmark requires the following configuration, which can be modified in `.env` file: + +``` +# Dify API配置 +API_KEY=your-api-key-here +DIFY_URL=https://api.dify.ai/v1/chat-messages + +# MyScale数据库配置 +MYSCALE_HOST=your-myscale-host +MYSCALE_PORT=8123 +MYSCALE_USER=your-myscale-username +MYSCALE_PASSWORD=your-myscale-password +MYSCALE_DATABASE=your-database-name + +# LLM API配置 +LLM_API_URL=your-llm-api-url +LLM_API_KEY=your-llm-api-key +LLM_MODEL=your-llm-model +LLM_EVALUATION_ENABLED=True +``` + +### Running the Benchmark + +You can run the benchmark using the following command: + +```bash +cd benchmark +python benchmark.py [options] +``` + +#### Command Line Options + +- `--dataset`: Path to the dataset file (default: `./data/results/test/olympics/olympics_qs.json`) +- `--output`: Path to save the results (default: `./results`) +- `--text-num`: Number of samples to test (default: all) +- `--no-llm`: Disable LLM evaluation (default: enabled) + +#### Examples + +1. Run with default settings: + ```bash + python benchmark.py + ``` + +2. Run with custom dataset and output path: + ```bash + python benchmark.py --dataset ./custom_dataset.json --output ./custom_results + ``` + +3. Run with only 50 samples and without LLM evaluation: + ```bash + python benchmark.py --text-num 50 --no-llm + ``` + +### Output + +The benchmark generates a JSON file with timestamp in the output directory (e.g., `benchmark_results-20260114-182456.json`). The output includes: + +- Summary statistics (total samples, success rate, average metrics) +- Detailed results for each sample (question, standard SQL, predicted SQL, evaluation metrics) + +## Evaluation + +The benchmark uses a comprehensive set of metrics to evaluate Text2SQL performance: + +### 1. Exact Match Metrics + +- **Exact Match**: Whether the predicted SQL exactly matches any of the ground truth SQL statements + +### 2. Set Metrics + +- **Precision**: The proportion of correctly predicted results among all predicted results +- **Recall**: The proportion of correctly predicted results among all ground truth results +- **F1 Score**: The harmonic mean of precision and recall + +### 3. Ranking Metrics + +- **MAP (Mean Average Precision)**: Average precision across all queries, considering the order of results +- **MRR (Mean Reciprocal Rank)**: Average of the reciprocals of the ranks of the first relevant result +- **NDCG (Normalized Discounted Cumulative Gain)**: Measures the ranking quality by discounting results further down the list + +### 4. LLM-Based Evaluation + +- **ACC_SQL**: Binary score (0/1) for SQL skeleton correctness evaluated by LLM +- **ACC_Vec**: Binary score (0/1) for vector component correctness evaluated by LLM +- **LLM Overall**: Average of ACC_SQL and ACC_Vec scores + +### Evaluation Process + +1. **SQL Extraction**: Extract SQL statements from MCP SQLVectorDB's natural language responses +2. **SQL Execution**: Execute both standard and predicted SQL on the MyScale database +3. **Result Comparison**: Compare execution results using set and ranking metrics +4. **LLM Evaluation**: (Optional) Use GPT-4o to evaluate SQL semantic correctness + +## Environment + +### System Requirements + +- **Operating System**: Linux/macOS/Windows +- **Architecture**: x86-64 (recommended) +- **Memory**: 8GB+ RAM +- **Storage**: 1GB+ free disk space + +### Python Dependencies + +- `requests`: For API calls +- `clickhouse_connect`: For connecting to MyScale database +- `argparse`: For command-line argument parsing +- `json`: For data handling +- `os`: For file system operations +- `datetime`: For timestamp generation + +### Database Requirements + +- **Database**: MyScale +- **Vector Index**: Pre-built vector indexes for efficient similarity search +- **Tables**: Database schema should match the test dataset requirements + +### API Requirements + +- **API**: Access to MCP SQLVectorDB's API with Text2SQL capabilities +- **LLM API**: Access to LLM model API with a valid API key +- **OpenAI API**: (Optional) For LLM-based evaluation using GPT-4o + +## Troubleshooting + +### Common Issues + +1. **API Connection Errors**: Verify your API key and network connectivity +2. **LLM API Errors**: Verify your LLM API key and network connectivity +3. **Database Errors**: Check MyScale connection parameters and database permissions +4. **SQL Execution Failures**: Ensure the database schema matches the expected structure +5. **LLM Evaluation Failures**: Verify OpenAI API access if using LLM evaluation + +### Logging + +Detailed logs are generated during benchmark execution, including: +- SQL execution results +- Evaluation metrics +- Error messages + +Logs can be found in the `log/` directory for debugging purposes. + +## License + +This benchmark is provided for evaluation purposes only. Please contact the maintainers for licensing information. + +## Contact + +For questions or issues, please contact the development team. \ No newline at end of file diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py new file mode 100644 index 0000000..2e68a08 --- /dev/null +++ b/benchmark/benchmark.py @@ -0,0 +1,396 @@ +import json +import os +import argparse +from datetime import datetime +from typing import List, Dict, Tuple, Optional, Any +from clickhouse_connect import get_client +from dotenv import load_dotenv + +from evaluation.metrics import extract_sql_from_dify_answer, evaluate_with_metrics +from tools.common import unify_lembed_clauses, get_dify_answer + +# 加载环境变量 +load_dotenv() + + +class Text2SQLBenchmark: + """ + Text2SQL Benchmark 类，用于评估 Dify Text2SQL 模型的性能 + """ + + def __init__( + self, + api_key: str, + dify_url: str, + myscale_host: str, + myscale_port: int, + myscale_user: str, + myscale_password: str, + myscale_database: str, + output_path: str = "./results", + llm_evaluation_enabled: bool = True, + llm_model: str = "gpt-4o", + ): + """ + 初始化 Text2SQLBenchmark 实例 + + Args: + api_key: Dify API 密钥 + dify_url: Dify API URL + myscale_host: MyScale 数据库主机 + myscale_port: MyScale 数据库端口 + myscale_user: MyScale 数据库用户名 + myscale_password: MyScale 数据库密码 + myscale_database: MyScale 数据库名称 + output_path: 结果输出路径 + llm_evaluation_enabled: 是否启用 LLM 评估 + llm_model: LLM 模型名称 + """ + self.api_key = api_key + self.dify_url = dify_url + self.myscale_host = myscale_host + self.myscale_port = myscale_port + self.myscale_user = myscale_user + self.myscale_password = myscale_password + self.myscale_database = myscale_database + self.output_path = output_path + self.llm_evaluation_enabled = llm_evaluation_enabled + self.llm_model = llm_model + + # 确保输出目录存在 + os.makedirs(self.output_path, exist_ok=True) + + def get_myscale_client(self): + """ + 获取 MyScale 数据库客户端 + + Returns: + MyScale 数据库客户端 + """ + return get_client( + host=self.myscale_host, + port=self.myscale_port, + user=self.myscale_user, + password=self.myscale_password, + database=self.myscale_database, + ) + + def run_sql_with_columns(self, sql: str) -> Tuple[List[tuple], List[str]]: + """ + 执行 SQL 查询并返回结果和列名 + + Args: + sql: SQL 查询语句 + + Returns: + (查询结果数据, 查询结果列名) + """ + client = None + try: + client = self.get_myscale_client() + result = client.query(sql) + + if not result.result_set: + return [], [] + + column_names = result.column_names + + distance_indices = [ + i for i, col in enumerate(column_names) if "distance" in col.lower() + ] + embedding_indices = [ + i for i, col in enumerate(column_names) if "embedding" in col.lower() + ] + exclude_indices = set(distance_indices + embedding_indices) + data = [] + for row in result.result_set: + filtered_row = tuple( + value for idx, value in enumerate(row) if idx not in exclude_indices + ) + data.append(filtered_row) + filtered_columns = [ + col for idx, col in enumerate(column_names) if idx not in exclude_indices + ] + + return data, filtered_columns + + except Exception as e: + print(f" ❌ SQL执行失败: {str(e)}") + return [], [] + finally: + if client: + client.close() + + def run_benchmark(self, dataset_path: str, text_num: Optional[int] = None) -> Dict[str, Any]: + """ + 运行 Text2SQL 基准测试 + + Args: + dataset_path: 数据集路径 + text_num: 测试样本数量（None 表示全部样本） + + Returns: + 基准测试结果 + """ + print("=" * 80) + print("🚀 开始 Dify Text2SQL Benchmark 测试") + print("=" * 80) + + with open(dataset_path, "r", encoding="utf-8") as f: + dataset = json.load(f) + + if text_num: + dataset = dataset[:text_num] + total_samples = len(dataset) + success_count = 0 + skipped_count = 0 + + db_schema = dataset[0].get("schema", "") if dataset else "" + + results = [] + all_eval_results = [] + + print(f"\n📊 数据集总样本数: {total_samples}\n") + + for i, sample in enumerate(dataset, 1): + question = sample.get("question", "") + standard_sql = sample.get("sql", "") + + if not question or not standard_sql: + print(f"⚠️ 样本 {i}: 缺少问题或SQL，跳过") + continue + + print(f"\n{'=' * 80}") + print(f"📝 样本 {i}/{total_samples}") + print(f"问题: {question}") + + print("\n🤖 步骤1: 调用Dify API获取回答...") + dify_answer = get_dify_answer(question, self.api_key, self.dify_url) + if dify_answer.startswith("ERROR:"): + print(f" ❌ Dify调用失败: {dify_answer}") + continue + + print(f" ✅ Dify回答获取成功: {dify_answer}") + + print(" 步骤2: 从Dify回答中提取SQL...") + predicted_sql = extract_sql_from_dify_answer(dify_answer) + if not predicted_sql: + print(" ❌ 无法从Dify回答中提取SQL") + continue + print(" ✅ 提取到预测SQL") + print(f" 标准SQL: {standard_sql}") + print(f" 预测SQL: {predicted_sql}") + + # 保存原始预测SQL用于比较 + original_predicted_sql = predicted_sql + + # 执行lembed子句统一处理 + predicted_sql = unify_lembed_clauses(standard_sql, predicted_sql) + + # 如果预测SQL发生了变化，输出信息 + if predicted_sql != original_predicted_sql: + print(" 🔄 统一了lembed子句") + print(f" 统一后预测SQL: {predicted_sql}") + + print(" 步骤3: 使用metrics.py进行评估...") + eval_results = evaluate_with_metrics( + run_sql_func=self.run_sql_with_columns, + nl_question=question, + standard_sql=standard_sql, + predicted_sql=predicted_sql, + db_schema=db_schema, + enable_llm=self.llm_evaluation_enabled, + ) + + if "error" in eval_results: + error_type = eval_results.get("error_type", "") + if error_type == "EMPTY_GOLDEN_DATA" or error_type == "EMPTY_TEST_DATA": + if error_type == "EMPTY_GOLDEN_DATA": + print(" ⏭️ 标准SQL无结果，跳过此样本") + else: + print(" ⏭️ 预测SQL执行失败或无结果，跳过此样本") + skipped_count += 1 + continue + print(f" ❌ 评估失败: {eval_results['error']}") + continue + + success_count += 1 + + result_item = { + "sample_id": i, + "question": question, + "standard_sql": standard_sql, + "predicted_sql": predicted_sql, + "dify_answer": dify_answer, + "evaluation": eval_results, + } + results.append(result_item) + all_eval_results.append(eval_results) + + print(" ✅ 评估结果:") + print(" 标准SQL执行结果:") + golden_data = eval_results.get("golden_data", []) + golden_columns = eval_results.get("golden_columns", []) + if golden_data: + print(f" 列名: {golden_columns}") + for row in golden_data[:5]: + print(f" {row}") + if len(golden_data) > 5: + print(f" ... 共 {len(golden_data)} 行") + else: + print(" (空结果)") + print(f" Exact Match: {eval_results.get('exact_match', 'N/A'):.3f}") + print(f" Precision: {eval_results.get('precision', 'N/A'):.3f}") + print(f" Recall: {eval_results.get('recall', 'N/A'):.3f}") + print(f" F1: {eval_results.get('f1', 'N/A'):.3f}") + print(f" MAP: {eval_results.get('map', 'N/A'):.3f}") + print(f" MRR: {eval_results.get('mrr', 'N/A'):.3f}") + print(f" NDCG: {eval_results.get('ndcg', 'N/A'):.3f}") + if "llm_overall_score" in eval_results: + print(f" LLM Overall: {eval_results['llm_overall_score']:.3f}") + + if i % 2 == 0: + print(f"len(all_eval_results): {len(all_eval_results)}") + avg_precision = sum(r.get("precision", 0) for r in all_eval_results) / len( + all_eval_results + ) + print( + f"\n 📊 已处理 {i}/{total_samples} 样本, 当前平均 Precision: {avg_precision:.4f}" + ) + avg_recall = sum(r.get("recall", 0) for r in all_eval_results) / len( + all_eval_results + ) + print( + f"\n 📊 已处理 {i}/{total_samples} 样本, 当前平均 Recall: {avg_recall:.3f}" + ) + avg_f1 = sum(r.get("f1", 0) for r in all_eval_results) / len(all_eval_results) + print(f"\n 📊 已处理 {i}/{total_samples} 样本, 当前平均 F1: {avg_f1:.4f}") + avg_mrr = sum(r.get("mrr", 0) for r in all_eval_results) / len(all_eval_results) + print( + f"\n 📊 已处理 {i}/{total_samples} 样本, 当前平均 MRR: {avg_mrr:.3f}" + ) + avg_llm_overall = sum( + r.get("llm_overall_score", 0) for r in all_eval_results + ) / len(all_eval_results) + print( + f"\n 📊 已处理 {i}/{total_samples} 样本, 当前平均 LLM Overall: {avg_llm_overall:.3f}" + ) + + print("\n" + "=" * 80) + print("📈 Benchmark 测试完成！") + print("=" * 80) + + if all_eval_results: + avg_precision = sum(r.get("precision", 0) for r in all_eval_results) / len( + all_eval_results + ) + avg_recall = sum(r.get("recall", 0) for r in all_eval_results) / len(all_eval_results) + avg_f1 = sum(r.get("f1", 0) for r in all_eval_results) / len(all_eval_results) + avg_exact_match = sum(r.get("exact_match", 0) for r in all_eval_results) / len( + all_eval_results + ) + avg_map = sum(r.get("map", 0) for r in all_eval_results) / len(all_eval_results) + avg_mrr = sum(r.get("mrr", 0) for r in all_eval_results) / len(all_eval_results) + avg_ndcg = sum(r.get("ndcg", 0) for r in all_eval_results) / len(all_eval_results) + else: + avg_precision = avg_recall = avg_f1 = avg_exact_match = avg_map = avg_mrr = avg_ndcg = 0 + + print(f"\n总样本数: {total_samples}") + print(f"成功评估样本数: {success_count}") + print(f"跳过样本数(标准SQL无结果): {skipped_count}") + print("\n🎯 平均评估指标:") + print(f" Exact Match: {avg_exact_match:.4f} ({avg_exact_match * 100:.2f}%)") + print(f" Precision: {avg_precision:.4f}") + print(f" Recall: {avg_recall:.4f}") + print(f" F1: {avg_f1:.4f}") + print(f" MAP: {avg_map:.4f}") + print(f" MRR: {avg_mrr:.4f}") + print(f" NDCG: {avg_ndcg:.4f}") + + time_suffix = datetime.now().strftime("%Y%m%d-%H%M%S") + output_path = os.path.join(self.output_path, f"benchmark_results-{time_suffix}.json") + + result_summary = { + "summary": { + "total_samples": total_samples, + "success_samples": success_count, + "llm_evaluation_enabled": self.llm_evaluation_enabled, + "metrics": { + "exact_match": avg_exact_match, + "precision": avg_precision, + "recall": avg_recall, + "f1": avg_f1, + "map": avg_map, + "mrr": avg_mrr, + "ndcg": avg_ndcg, + }, + }, + "details": results, + } + + with open(output_path, "w", encoding="utf-8") as f: + json.dump(result_summary, f, ensure_ascii=False, indent=2) + + print(f"\n💾 详细结果已保存至: {output_path}") + print("=" * 80) + + return result_summary + + +# ========== 配置 ========== +# 从环境变量加载配置 +API_KEY = os.getenv("API_KEY") +DIFY_URL = os.getenv("DIFY_URL", "https://api.dify.ai/v1/chat-messages") +MYSCALE_HOST = os.getenv("MYSCALE_HOST") +MYSCALE_PORT = int(os.getenv("MYSCALE_PORT")) +MYSCALE_USER = os.getenv("MYSCALE_USER") +MYSCALE_PASSWORD = os.getenv("MYSCALE_PASSWORD") +MYSCALE_DATABASE = os.getenv("MYSCALE_DATABASE", "olympics") + +DEFAULT_DATASET_PATH = "./data/results/test/olympics/olympics_qs.json" +DEFAULT_OUTPUT_PATH = "./result/" +LLM_EVALUATION_ENABLED = True +LLM_MODEL = "gpt-4o" + + +# ========== 主程序 ========== +def main(): + # 解析命令行参数 + parser = argparse.ArgumentParser(description="Dify Text2SQL Benchmark 测试") + parser.add_argument( + "--dataset", + type=str, + default=DEFAULT_DATASET_PATH, + help="数据集路径 (默认: %s)" % DEFAULT_DATASET_PATH, + ) + parser.add_argument( + "--output", + type=str, + default=DEFAULT_OUTPUT_PATH, + help="结果输出路径 (默认: %s)" % DEFAULT_OUTPUT_PATH, + ) + parser.add_argument("--text-num", type=int, default=None, help="测试样本数量 (默认: 全部)") + parser.add_argument("--no-llm", action="store_true", help="禁用 LLM 评估") + + args = parser.parse_args() + + # 创建基准测试实例 + benchmark = Text2SQLBenchmark( + api_key=API_KEY, + dify_url=DIFY_URL, + myscale_host=MYSCALE_HOST, + myscale_port=MYSCALE_PORT, + myscale_user=MYSCALE_USER, + myscale_password=MYSCALE_PASSWORD, + myscale_database=MYSCALE_DATABASE, + output_path=args.output, + llm_evaluation_enabled=not args.no_llm, + llm_model=LLM_MODEL, + ) + + # 运行基准测试 + benchmark.run_benchmark(args.dataset, args.text_num) + + +if __name__ == "__main__": + main() diff --git a/benchmark/data/READMD.md b/benchmark/data/READMD.md new file mode 100644 index 0000000..652dd9d --- /dev/null +++ b/benchmark/data/READMD.md @@ -0,0 +1,17 @@ +# How to Generate the Dataset +The dataset generation method is derived from the dataset generation approach in https://github.com/OpenDCAI/Text2VectorSQL. + +The detailed process is as follows: +1. Select the required dataset in the directory `Text2VectorSQL/Data_Synthesizer/pipeline/sqlite/train`. +2. Modify the configuration of the corresponding `Text2VectorSQL/Data_Synthesizer/pipeline/config.yaml` file. +3. Run `Text2VectorSQL/Data_Synthesizer/pipeline/general_pipeline.py` (you need to enable the corresponding operators in the file). This step is used to generate the SQLite database and the corresponding SQL statements. +4. Execute the script in `Text2VectorSQL/Data_Synthesizer/tools` to migrate the data from SQLite to the target database. + Example command: + ```bash + python migrate_db_myscale.py --source /mnt/DataFlow/ydw/Text2VectorSQL/Data_Synthesizer/pipeline/sqlite/results/test/vector_databases --host xxxxxx --port 9000 --user default --password "xxxxx" + ``` +5. Execute the script to convert SQLite SQL statements to the corresponding target SQL statements. After execution, the required dataset file `candidate_sql.json` will be generated in the directory `/Data_Synthesizer/pipeline/myscale/results/test/` under the selected target database. + Example command: + ```bash + python migrate_main_sql_only.py --workers 32 --myscale_password 'myscale#EDC' --datasets test + ``` \ No newline at end of file diff --git a/benchmark/data/results/arxiv/candidate_sql.json b/benchmark/data/results/arxiv/candidate_sql.json new file mode 100644 index 0000000..9adbfd4 --- /dev/null +++ b/benchmark/data/results/arxiv/candidate_sql.json @@ -0,0 +1,1938 @@ +[ + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced algorithm in quantum physics') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT\n a.id AS id,\n a.abstract AS abstract,\n a.title AS title,\n a.update_date AS update_date,\n distance(a.abstract_embedding, ref_vec_0) AS abstract_distance\n FROM articles a\n ORDER BY abstract_distance\n LIMIT 10\n),\n\nAuthoredArticles AS (\n SELECT\n sa.id AS id,\n sa.title AS title,\n sa.abstract_distance AS abstract_distance,\n au.name AS author_name\n FROM SimilarAbstracts sa\n JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id)\n JOIN authors au ON toString(aa.author_id) = toString(au.id)\n),\n\nVersionedCategories AS (\n SELECT\n aa.id AS id,\n aa.title AS title,\n aa.abstract_distance AS abstract_distance,\n aa.author_name AS author_name,\n v.version_num AS version_num,\n v.created AS created,\n c.code AS category_code\n FROM AuthoredArticles aa\n JOIN versions v ON toString(aa.id) = toString(v.article_id)\n JOIN article_categories ac ON toString(aa.id) = toString(ac.article_id)\n JOIN categories c ON toString(ac.category_id) = toString(c.id)\n)\n\nSELECT\n vc.title AS title\nFROM VersionedCategories vc\nWHERE vc.version_num = (\n SELECT MAX(v.version_num)\n FROM versions v\n WHERE v.article_id = vc.id\n)\nORDER BY vc.abstract_distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles of the 5 most relevant articles concerning advanced algorithms in quantum physics, ensuring each one has the latest version?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge quantum physics algorithms') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, a.title, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS abstract_distance FROM articles a\n ORDER BY abstract_distance\n LIMIT 10\n),\n\nAuthoredArticles AS (\n SELECT sa.id, sa.title, sa.abstract_distance, au.name AS author_name FROM SimilarAbstracts sa JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\n),\n\nVersionedCategories AS (\n SELECT aa.id, aa.title, aa.abstract_distance, aa.author_name, v.version_num, v.created, c.code AS category_code FROM AuthoredArticles aa JOIN versions v ON toString(aa.id) = toString(v.article_id) JOIN article_categories ac ON toString(aa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\n)\n\nSELECT vc.title FROM VersionedCategories vc WHERE vc.version_num = ( SELECT MAX(v.version_num) FROM versions v WHERE v.article_id = vc.id ) ORDER BY vc.abstract_distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Latest advancements in quantum physics algorithms') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, a.title, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS abstract_distance FROM articles a\n ORDER BY abstract_distance\n LIMIT 10\n),\n\nAuthoredArticles AS (\n SELECT sa.id, sa.title, sa.abstract_distance, au.name AS author_name FROM SimilarAbstracts sa JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\n),\n\nVersionedCategories AS (\n SELECT aa.id, aa.title, aa.abstract_distance, aa.author_name, v.version_num, v.created, c.code AS category_code FROM AuthoredArticles aa JOIN versions v ON toString(aa.id) = toString(v.article_id) JOIN article_categories ac ON toString(aa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\n)\n\nSELECT vc.title FROM VersionedCategories vc WHERE vc.version_num = ( SELECT MAX(v.version_num) FROM versions v WHERE v.article_id = vc.id ) ORDER BY vc.abstract_distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Contemporary quantum physics algorithm techniques') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, a.title, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS abstract_distance FROM articles a\n ORDER BY abstract_distance\n LIMIT 10\n),\n\nAuthoredArticles AS (\n SELECT sa.id, sa.title, sa.abstract_distance, au.name AS author_name FROM SimilarAbstracts sa JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\n),\n\nVersionedCategories AS (\n SELECT aa.id, aa.title, aa.abstract_distance, aa.author_name, v.version_num, v.created, c.code AS category_code FROM AuthoredArticles aa JOIN versions v ON toString(aa.id) = toString(v.article_id) JOIN article_categories ac ON toString(aa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\n)\n\nSELECT vc.title FROM VersionedCategories vc WHERE vc.version_num = ( SELECT MAX(v.version_num) FROM versions v WHERE v.article_id = vc.id ) ORDER BY vc.abstract_distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum physics algorithm innovations') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, a.title, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS abstract_distance FROM articles a\n ORDER BY abstract_distance\n LIMIT 10\n),\n\nAuthoredArticles AS (\n SELECT sa.id, sa.title, sa.abstract_distance, au.name AS author_name FROM SimilarAbstracts sa JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\n),\n\nVersionedCategories AS (\n SELECT aa.id, aa.title, aa.abstract_distance, aa.author_name, v.version_num, v.created, c.code AS category_code FROM AuthoredArticles aa JOIN versions v ON toString(aa.id) = toString(v.article_id) JOIN article_categories ac ON toString(aa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\n)\n\nSELECT vc.title FROM VersionedCategories vc WHERE vc.version_num = ( SELECT MAX(v.version_num) FROM versions v WHERE v.article_id = vc.id ) ORDER BY vc.abstract_distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent quantum physics algorithm developments') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, a.title, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS abstract_distance FROM articles a\n ORDER BY abstract_distance\n LIMIT 10\n),\n\nAuthoredArticles AS (\n SELECT sa.id, sa.title, sa.abstract_distance, au.name AS author_name FROM SimilarAbstracts sa JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\n),\n\nVersionedCategories AS (\n SELECT aa.id, aa.title, aa.abstract_distance, aa.author_name, v.version_num, v.created, c.code AS category_code FROM AuthoredArticles aa JOIN versions v ON toString(aa.id) = toString(v.article_id) JOIN article_categories ac ON toString(aa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\n)\n\nSELECT vc.title FROM VersionedCategories vc WHERE vc.version_num = ( SELECT MAX(v.version_num) FROM versions v WHERE v.article_id = vc.id ) ORDER BY vc.abstract_distance LIMIT 5;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'vc.id' while processing query: 'WITH [-0.0875653624534607, 0.06992679089307785, -0.006955740507692099, 0.029801499098539352, -0.09842625260353088, -0.04059459641575813, -0.010832546278834343, -0.02275134064257145, -0.11314383894205093, -0.005256382282823324, -0.0007435149163939059, -0.045022137463092804, -0.023426856845617294, -0.005216408520936966, -0.05445540323853493, 0.08048360794782639, -0.013329784385859966, 0.017371956259012222, -0.004621555097401142, -0.11528845876455307, -0.06764446198940277, 0.04365556314587593, 0.001283635850995779, -0.030244365334510803, 0.09868042916059494, -0.005433236714452505, 0.053147654980421066, -0.024295957759022713, 0.07106677442789078, 0.0024139501620084047, 0.0624006986618042, 0.0033546024933457375, 0.029408851638436317, 0.003548608161509037, -0.07500708103179932, 0.012086962349712849, 0.003404180519282818, -0.023065786808729172, 0.041112031787633896, 0.020004939287900925, -0.016798101365566254, 0.0922006294131279, -0.04404754936695099, -0.007366478443145752, 0.04880807176232338, 0.09154021739959717, -0.05814862623810768, 0.041580263525247574, 0.004774490371346474, -0.09798260033130646, -0.0012874787207692862, 0.034277185797691345, 0.007910732179880142, 0.039595041424036026, 0.024038271978497505, -0.026652198284864426, 0.058439332991838455, -0.09399357438087463, -0.022302936762571335, -0.07328915596008301, 0.023742882534861565, -0.004807824268937111, -0.03487422689795494, 0.017374426126480103, 0.11043421179056168, 0.01256090123206377, 0.013037226162850857, -0.02612490765750408, -0.009490675292909145, 0.060227587819099426, -0.07566624134778976, -0.002291756449267268, -0.014906836673617363, 0.007678872440010309, 0.04817728325724602, -0.020555146038532257, 0.03467997536063194, 0.04816184192895889, -0.02088957652449608, 0.04005127772688866, 0.02327398955821991, -0.09761379659175873, -0.016736874356865883, -0.036244165152311325, 0.05899015814065933, -0.007168880198150873, 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-0.0063257962465286255, 0.014166167005896568, 0.02034534327685833, 0.08566661924123764, -0.06096724793314934, -0.05156125873327255, -0.02053891122341156, 0.039976201951503754, 0.01864606887102127, 0.017849721014499664, -0.07658921182155609, 0.0063813659362494946, -0.05527349188923836, -0.015204721130430698, -0.007879741489887238, -0.004439933225512505, -0.032864365726709366, 0.07088939100503922, -0.005809819791465998, -0.02164594829082489, -0.010250581428408623, -0.06182454153895378, 0.011738966219127178, 0.10560821741819382, -0.040735822170972824, -0.017332687973976135, 0.0012232450535520911, -0.005690193269401789, 0.07179577648639679, -0.09576447308063507, 0.0035626920871436596, 0.030701063573360443, 0.007365006487816572, 0.015818729996681213, 0.019320594146847725, 0.02108507975935936, -0.04531656950712204, -0.013495304621756077, 0.016507603228092194, 0.04586721584200859, -0.06736433506011963, -0.028423147276043892, 0.046772826462984085, -0.0033596737775951624, 0.01538767572492361, 0.08137740939855576, 0.06280778348445892, 0.031316567212343216, -0.11173953115940094, -0.07522080093622208, 0.023612992838025093, 0.014199711382389069, -0.022608544677495956, 0.0378931388258934, 0.020168988034129143, 0.014661179855465889, 0.016713345423340797, 0.0122338542714715, 0.0003982970374636352, -0.01686077192425728, 0.010969883762300014, -0.06969378143548965, -0.0107034333050251, -0.03584016487002373, -0.09214860945940018, 0.020149588584899902, 0.15930978953838348, -0.024295730516314507, 0.06236700341105461, 0.07936275005340576, -0.042069338262081146, 0.06205631047487259, -0.1041889637708664, 0.03183552622795105, -0.09454620629549026, -0.0045311967842280865, -0.04458137974143028, -0.04911256581544876, 0.02060699835419655, 0.010254250839352608, 0.04694778844714165, 0.007651429623365402, -0.1322401762008667, -0.10932980477809906, 0.02657383866608143, -0.039510101079940796, -0.031171632930636406, -0.07904943823814392, -0.05376718193292618, 0.0967131108045578, 0.06610929220914841, -0.03975618630647659, 1.2802926109339715e-33, -0.05270784720778465, 0.023890476673841476, 0.0850539281964302, 0.022984161972999573, 0.04437674954533577, -0.06772446632385254, -0.02943580225110054, -0.05380148068070412, 0.061378758400678635, 0.00231301155872643, 0.037412066012620926, 0.07240133732557297, 0.06277857720851898, 0.08075069636106491, 0.05248437076807022, 0.049133215099573135, 0.026563821360468864, -0.0033188611268997192, 0.09288700670003891, 0.03113936260342598, 0.029797280207276344, -0.006748806219547987, 0.01512946654111147, -0.06542003154754639, 0.01315503753721714, 0.04794737696647644, 0.11047719419002533, -0.0025547791738063097, 0.027006905525922775, -0.07289999723434448, -0.0844491720199585, -0.06353605538606644, -0.0612078458070755, 0.01881997287273407, -0.11795545369386673, 0.049070753157138824, 0.04517948254942894, 0.06532084941864014, 0.0415150411427021, -0.03681226819753647, -0.020005498081445694, 0.022547908127307892, -0.02743815816938877, -0.03606012463569641, 0.05909857898950577, 0.04051157087087631, -0.008973079733550549, 0.07447939366102219, -0.08913228660821915, 0.056593701243400574, -0.003657445777207613, -0.023323316127061844, 0.010354637168347836, 0.07101059705018997, -0.06076681241393089, 0.09093454480171204, -0.04889780282974243, 0.08593080937862396, 0.06222645193338394, -0.0033909534104168415, -0.07587050646543503, 0.005638683680444956, 0.0480651929974556, 0.09526299685239792, -0.016958434134721756, -0.03808913007378578, -0.054895054548978806, 0.012265621684491634, -0.044277921319007874, -0.00099285994656384, -0.013080055825412273, 0.03980954736471176, 0.012264392338693142, 0.011185572482645512, 0.024578750133514404, -0.04326489195227623, 0.03581634908914566, -0.03582938760519028, 0.07169107347726822, 0.0010049504926428199, -0.02853304147720337, 0.044239871203899384, -0.039541926234960556, 0.020325668156147003, 0.010902821086347103, -0.009089188650250435, 0.058791499584913254, -0.004829954821616411, 0.05069470405578613, -0.04661761596798897, -0.01453118771314621, 0.04471394792199135, 0.0713982805609703, -0.06186074763536453, 0.031964387744665146, -1.2640747115710838e-8, 0.03004917874932289, -0.10284221172332764, -0.08829249441623688, -0.04843360558152199, 0.14885908365249634, -0.041402608156204224, 0.02364339306950569, -0.01887284778058529, -0.06269662082195282, -0.1503886580467224, 0.026792142540216446, -0.07349737733602524, -0.008444756269454956, 0.05116073042154312, 0.016166526824235916, 0.0290732029825449, -0.05248802900314331, -0.07130129635334015, -0.03689737617969513, -0.012494456022977829, 0.010577044449746609, 0.01661263033747673, 0.09223518520593643, 0.012521892786026001, -0.025323234498500824, -0.018593680113554, -0.03410082682967186, -0.10241666436195374, 0.035912975668907166, 0.01644771732389927, -0.053539399057626724, 0.01769411750137806, 0.051272496581077576, 0.10290191322565079, -0.02477463334798813, -0.03272527828812599, -0.030715027824044228, -0.024145884439349174, -0.05957389250397682, 0.04193532094359398, -0.05381226912140846, 0.04338310286402702, 0.01819649338722229, -0.012778966687619686, 0.08315641433000565, 0.05126150697469711, 0.020614825189113617, -0.004498521331697702, 0.05217744782567024, 0.08404958248138428, -0.0008344457019120455, 0.012757128104567528, 0.004659814760088921, -0.1052292138338089, -0.051332686096429825, 0.024970827624201775, -0.013306980952620506, -0.03893599659204483, 0.042129773646593094, -0.017787780612707138, 0.021441686898469925, 0.004994043614715338, -0.04508606716990471, 0.025017336010932922] AS ref_vec_0 SELECT max(version_num) FROM versions AS v WHERE article_id = vc.id', required columns: 'article_id' 'vc.id' 'version_num', maybe you meant: 'article_id' or 'version_num': While processing (WITH [-0.0875653624534607, 0.06992679089307785, -0.006955740507692099, 0.029801499098539352, -0.09842625260353088, -0.04059459641575813, -0.010832546278834343, -0.02275134064257145, -0.11314383894205093, -0.005256382282823324, 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0.01661263033747673, 0.09223518520593643, 0.012521892786026001, -0.025323234498500824, -0.018593680113554, -0.03410082682967186, -0.10241666436195374, 0.035912975668907166, 0.01644771732389927, -0.053539399057626724, 0.01769411750137806, 0.051272496581077576, 0.10290191322565079, -0.02477463334798813, -0.03272527828812599, -0.030715027824044228, -0.024145884439349174, -0.05957389250397682, 0.04193532094359398, -0.05381226912140846, 0.04338310286402702, 0.01819649338722229, -0.012778966687619686, 0.08315641433000565, 0.05126150697469711, 0.020614825189113617, -0.004498521331697702, 0.05217744782567024, 0.08404958248138428, -0.0008344457019120455, 0.012757128104567528, 0.004659814760088921, -0.1052292138338089, -0.051332686096429825, 0.024970827624201775, -0.013306980952620506, -0.03893599659204483, 0.042129773646593094, -0.017787780612707138, 0.021441686898469925, 0.004994043614715338, -0.04508606716990471, 0.025017336010932922] AS ref_vec_0 SELECT max(v.version_num) FROM versions AS v WHERE v.article_id = vc.id) AS _subquery50). (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in collider physics') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code = 'physics'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Top 5 articles in physics related to Quantum chromodynamics in collider physics.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and collider experiments') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Collider physics with quantum chromodynamics focus') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in particle colliders') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-energy physics: Quantum chromodynamics') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and collider studies') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pairs production at colliders') AS ref_vec_0,\n\nRankedArticles AS (\n SELECT \n a.id AS id,\n a.title AS title,\n a.abstract AS abstract,\n distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM \n articles a\n ORDER BY distance\n LIMIT 10\n),\n\nTopRanked AS (\n SELECT \n ra.id AS id,\n ra.title AS title,\n ra.abstract AS abstract,\n ROW_NUMBER() OVER (ORDER BY ra.distance) as rank\n FROM \n RankedArticles ra\n),\n\nAuthorsInfo AS (\n SELECT \n ta.id as article_id,\n GROUP_CONCAT(au.name, ', ') as authors\n FROM \n TopRanked ta\n JOIN \n article_authors aa ON toString(ta.id) = toString(aa.article_id)\n JOIN \n authors au ON toString(aa.author_id) = toString(au.id)\n GROUP BY \n ta.id AS id\n)\n\nSELECT \n tr.title AS title,\n ai.authors AS authors,\n tr.abstract AS abstract\nFROM \n TopRanked tr\nJOIN \n AuthorsInfo ai ON toString(tr.id) = toString(ai.article_id)\nWHERE \n tr.rank <= 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the top 5 articles concerning \"Quantum chromodynamics and photon pairs production at colliders.\" Provide the articles' titles, authors, and abstracts.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and production of photon pairs in collider experiments') AS ref_vec_0,\n\nRankedArticles AS (\n SELECT a.id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nTopRanked AS (\n SELECT ra.id, ra.title, ra.abstract, ROW_NUMBER() OVER (ORDER BY ra.distance) as rank FROM RankedArticles ra\n),\n\nAuthorsInfo AS (\n SELECT ta.id as article_id, GROUP_CONCAT(au.name, ', ') as authors FROM TopRanked ta JOIN article_authors aa ON toString(ta.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) GROUP BY ta.id\n)\n\nSELECT tr.title, ai.authors, tr.abstract FROM TopRanked tr JOIN AuthorsInfo ai ON toString(tr.id) = toString(ai.article_id) WHERE tr.rank <= 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pair production and quantum chromodynamics in collider settings') AS ref_vec_0,\n\nRankedArticles AS (\n SELECT a.id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nTopRanked AS (\n SELECT ra.id, ra.title, ra.abstract, ROW_NUMBER() OVER (ORDER BY ra.distance) as rank FROM RankedArticles ra\n),\n\nAuthorsInfo AS (\n SELECT ta.id as article_id, GROUP_CONCAT(au.name, ', ') as authors FROM TopRanked ta JOIN article_authors aa ON toString(ta.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) GROUP BY ta.id\n)\n\nSELECT tr.title, ai.authors, tr.abstract FROM TopRanked tr JOIN AuthorsInfo ai ON toString(tr.id) = toString(ai.article_id) WHERE tr.rank <= 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Collider studies on quantum chromodynamics and photon pair events') AS ref_vec_0,\n\nRankedArticles AS (\n SELECT a.id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nTopRanked AS (\n SELECT ra.id, ra.title, ra.abstract, ROW_NUMBER() OVER (ORDER BY ra.distance) as rank FROM RankedArticles ra\n),\n\nAuthorsInfo AS (\n SELECT ta.id as article_id, GROUP_CONCAT(au.name, ', ') as authors FROM TopRanked ta JOIN article_authors aa ON toString(ta.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) GROUP BY ta.id\n)\n\nSELECT tr.title, ai.authors, tr.abstract FROM TopRanked tr JOIN AuthorsInfo ai ON toString(tr.id) = toString(ai.article_id) WHERE tr.rank <= 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on photon pair production and QCD at colliders') AS ref_vec_0,\n\nRankedArticles AS (\n SELECT a.id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nTopRanked AS (\n SELECT ra.id, ra.title, ra.abstract, ROW_NUMBER() OVER (ORDER BY ra.distance) as rank FROM RankedArticles ra\n),\n\nAuthorsInfo AS (\n SELECT ta.id as article_id, GROUP_CONCAT(au.name, ', ') as authors FROM TopRanked ta JOIN article_authors aa ON toString(ta.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) GROUP BY ta.id\n)\n\nSELECT tr.title, ai.authors, tr.abstract FROM TopRanked tr JOIN AuthorsInfo ai ON toString(tr.id) = toString(ai.article_id) WHERE tr.rank <= 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics and photon pairs in collider physics') AS ref_vec_0,\n\nRankedArticles AS (\n SELECT a.id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nTopRanked AS (\n SELECT ra.id, ra.title, ra.abstract, ROW_NUMBER() OVER (ORDER BY ra.distance) as rank FROM RankedArticles ra\n),\n\nAuthorsInfo AS (\n SELECT ta.id as article_id, GROUP_CONCAT(au.name, ', ') as authors FROM TopRanked ta JOIN article_authors aa ON toString(ta.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) GROUP BY ta.id\n)\n\nSELECT tr.title, ai.authors, tr.abstract FROM TopRanked tr JOIN AuthorsInfo ai ON toString(tr.id) = toString(ai.article_id) WHERE tr.rank <= 5;" + ], + "integration_level": 2, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 42, server response: Code: 42. DB::Exception: Aggregate function groupConcat requires single argument. (NUMBER_OF_ARGUMENTS_DOESNT_MATCH) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'novel algorithm in graph theory') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find five articles that are really diving into innovative methods within graph theory, specifically in data structures?", + "external_knowledge": "The vector search operation in the query uses the `MATCH` operator, which performs approximate nearest neighbor (ANN) search. The `lembed()` function with the `all-MiniLM-L6-v2` model transforms the text \"novel algorithm in graph theory\" into a vector embedding. The search retrieves articles whose abstract embeddings are closest to this vector, measured by Euclidean distance. The `k = 5` condition limits the search to the top 5 similar results, implying the articles most relevant to innovative graph theory algorithms within the domain of data structures.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'innovative techniques in graph theory data structures') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'advanced methods in graph theory for data structures') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'cutting-edge approaches in graph theory related to data structures') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'exploratory methods in graph theory and data structures') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'novel techniques in graph theory focusing on data structures') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative algorithms in graph theory and their applications') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nWHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you tell me the titles of the five articles related to groundbreaking algorithms in graph theory that John Doe submitted?", + "external_knowledge": "The `MATCH` operator is used for approximate nearest neighbor (ANN) search within vector embeddings, which helps identify items closely related in meaning to a given query. The vector embeddings are compared using Euclidean distance (L2 norm), where smaller distances indicate greater similarity. The `k = 5` specifies that the query seeks to find the top 5 articles that best match the semantic context of \"Innovative algorithms in graph theory and their applications\", focusing on articles submitted by John Doe.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Groundbreaking graph theory algorithms') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Revolutionary methods in graph theory') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel approaches to graph theory algorithms') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced graph theory algorithm developments') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative graph theory algorithm submissions') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of machine learning techniques in natural language processing') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nWHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles submitted by John Doe that dive into exploring machine learning techniques in natural language processing? I’d love to know their titles!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigating ML methods for NLP applications') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring AI techniques for processing human language') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Machine learning strategies in NLP research') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced ML approaches in natural language understanding') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods in machine learning for NLP') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A fully differential calculation in perturbative quantum chromodynamics is presented for the production of massive photon pairs at hadron colliders.') AS ref_vec_0\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE au.name IN ('John Doe', 'Jane Smith')\nAND c.code IN ('physics', 'mathematics')\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which are the top five articles, authored by John Doe or Jane Smith, that delve into complex physics and math topics related to massive photon pairs at hadron colliders?", + "external_knowledge": "The `MATCH` operator in the SQL query conducts an approximate nearest neighbor (ANN) search, which is a technique used to find data points that are closest to a query point in a high-dimensional space. The `lembed` function utilizes embeddings from the 'all-MiniLM-L6-v2' model to represent the query concept as a vector, allowing for similarity comparisons against the article abstracts. The parameter `k=5` specifies that the search should return the top five most similar articles based on this vector representation. In vector searches, similarity is typically measured using the Euclidean distance (L2 norm), which means that articles with smaller distance values are considered more similar to the search concept.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of complex physics and mathematical principles involving massive photon pair interactions at hadron colliders.') AS ref_vec_0\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE au.name IN ('John Doe', 'Jane Smith') AND c.code IN ('physics', 'mathematics')\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth analysis of massive photon pairs at hadron colliders within the realm of physics and advanced mathematics.') AS ref_vec_0\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE au.name IN ('John Doe', 'Jane Smith') AND c.code IN ('physics', 'mathematics')\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical insights into massive photon pair production at hadron colliders through complex physics and mathematics.') AS ref_vec_0\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE au.name IN ('John Doe', 'Jane Smith') AND c.code IN ('physics', 'mathematics')\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced study of massive photon pairs in the context of quantum chromodynamics and mathematical frameworks at hadron colliders.') AS ref_vec_0\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE au.name IN ('John Doe', 'Jane Smith') AND c.code IN ('physics', 'mathematics')\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Complex analysis of photon pair interactions at hadron colliders involving advanced physics and mathematical concepts.') AS ref_vec_0\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE au.name IN ('John Doe', 'Jane Smith') AND c.code IN ('physics', 'mathematics')\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A characterization of the family of sparse graphs and algorithmic solutions concerning tree decompositions') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the article that best matches the description \"A characterization of the family of sparse graphs and algorithmic solutions concerning tree decompositions\" and provide its title.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Characterization of sparse graph families and tree decomposition algorithms') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graphs characterization and tree decomposition methods') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Understanding sparse graphs and related tree decomposition techniques') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graph family characterizations and algorithmic tree decomposition solutions') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Algorithmic approaches to sparse graphs and tree decomposition characterization') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Perturbative quantum chromodynamics and massive photon pairs production') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the article that best relates to \"Perturbative quantum chromodynamics and massive photon pairs production\", including its ID and title?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics perturbation and production of photon pairs') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Massive photon pairs generation in perturbative quantum chromodynamics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Perturbative QCD and photon pair creation') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pair production') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Production of photon pairs through perturbative quantum chromodynamics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics explores the behavior of matter and energy at the smallest scales.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the IDs of the top 5 articles that are most relevant to the topic of quantum mechanics?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics studies the fundamental principles governing the micro-world.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics investigates atomic and subatomic particles and their interactions.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics involves understanding matter and energy at quantum levels.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The field of quantum mechanics deals with the behavior of particles at the quantum scale.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics is concerned with the laws of physics governing the smallest particles.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of graph decompositions and sparse graph algorithms.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you hook me up with the IDs and titles of the top 5 articles that dive into graph decompositions and sparse graph algorithms?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top articles on graph decompositions and algorithms for sparse graphs.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading studies in graph decompositions and sparse graph techniques.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth analysis of graph decomposition methods and sparse graph algorithms.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research articles focusing on graph decomposition and sparse graph strategies.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Insights into graph decomposition and algorithms for sparse graphs.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A breakthrough in artificial intelligence for solving complex problems') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id\n FROM article_categories ac\n JOIN categories c ON toString(ac.category_id) = toString(c.id)\n WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the titles of the top 5 articles categorized under 'Artificial Intelligence' that showcase a breakthrough in AI for solving complex problems.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative AI solutions for complex problem solving') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'AI advancements in tackling challenging issues') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Breakthrough AI methods for addressing complex challenges') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced AI techniques for solving difficult problems') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Revolutionary AI approaches to complex problem solving') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'quantum mechanics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'quantum mechanics') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'quantum mechanics') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(comments_embedding, ref_vec_2) AS distance\n FROM articles\n WHERE title_embedding MATCH lembed('all-MiniLM-L6-v2', 'quantum mechanics')\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT \n a.id AS id, \n a.title AS title, \n v.version_num AS version_num, \n v.created AS created, \n a.distance AS distance\n FROM a_filtered AS a\n JOIN versions v ON toString(a.id) = toString(v.article_id)\n)\n\nSELECT \n am.title AS title,\n COUNT(am.version_num) AS version_count\nFROM ArticleMatches am\nWHERE am.created >= '2023-01-01'\nGROUP BY am.title\nORDER BY version_count DESC;", + "sql_result_column_count": 2, + "sql_result_rows_count": 30, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you tell me which articles related to \"quantum mechanics\" have been created since January 1, 2023, and list them by the number of updates they have received?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'quantum physics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'quantum physics') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'quantum physics') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(comments_embedding, ref_vec_2) AS distance\n FROM articles\n WHERE title_embedding MATCH lembed('all-MiniLM-L6-v2', 'quantum physics')\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT a.id, a.title, v.version_num, v.created, a.distance FROM a_filtered AS a JOIN versions v ON toString(a.id) = toString(v.article_id)\n)\n\nSELECT am.title, COUNT(am.version_num) AS version_count FROM ArticleMatches am WHERE am.created >= '2023-01-01' GROUP BY am.title ORDER BY version_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'quantum theory') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'quantum theory') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'quantum theory') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(comments_embedding, ref_vec_2) AS distance\n FROM articles\n WHERE title_embedding MATCH lembed('all-MiniLM-L6-v2', 'quantum theory')\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT a.id, a.title, v.version_num, v.created, a.distance FROM a_filtered AS a JOIN versions v ON toString(a.id) = toString(v.article_id)\n)\n\nSELECT am.title, COUNT(am.version_num) AS version_count FROM ArticleMatches am WHERE am.created >= '2023-01-01' GROUP BY am.title ORDER BY version_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'quantum science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'quantum science') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'quantum science') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(comments_embedding, ref_vec_2) AS distance\n FROM articles\n WHERE title_embedding MATCH lembed('all-MiniLM-L6-v2', 'quantum science')\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT a.id, a.title, v.version_num, v.created, a.distance FROM a_filtered AS a JOIN versions v ON toString(a.id) = toString(v.article_id)\n)\n\nSELECT am.title, COUNT(am.version_num) AS version_count FROM ArticleMatches am WHERE am.created >= '2023-01-01' GROUP BY am.title ORDER BY version_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'quantum studies') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'quantum studies') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'quantum studies') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(comments_embedding, ref_vec_2) AS distance\n FROM articles\n WHERE title_embedding MATCH lembed('all-MiniLM-L6-v2', 'quantum studies')\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT a.id, a.title, v.version_num, v.created, a.distance FROM a_filtered AS a JOIN versions v ON toString(a.id) = toString(v.article_id)\n)\n\nSELECT am.title, COUNT(am.version_num) AS version_count FROM ArticleMatches am WHERE am.created >= '2023-01-01' GROUP BY am.title ORDER BY version_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'quantum phenomena') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'quantum phenomena') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'quantum phenomena') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(comments_embedding, ref_vec_2) AS distance\n FROM articles\n WHERE title_embedding MATCH lembed('all-MiniLM-L6-v2', 'quantum phenomena')\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT a.id, a.title, v.version_num, v.created, a.distance FROM a_filtered AS a JOIN versions v ON toString(a.id) = toString(v.article_id)\n)\n\nSELECT am.title, COUNT(am.version_num) AS version_count FROM ArticleMatches am WHERE am.created >= '2023-01-01' GROUP BY am.title ORDER BY version_count DESC;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 25499 ('MATCH') (line 11, col 27): MATCH [-0.06727885454893112, 0.009492035955190659, -0.02748350240290165, 0.0815303847193718, -0.12409452348947525, 0.0638880729675293, 0.03412516042590141, -0.0. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics principles and applications in modern physics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the title and similarity score for the article most related to exploring quantum mechanics principles and applications in modern physics?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics principles and their role in modern physics applications') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Understanding quantum mechanics and its applications in today’s physics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring the principles of quantum mechanics in contemporary physics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Principles and applications of quantum mechanics in modern scientific contexts') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Modern physics and the exploration of quantum mechanics principles') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'This abstract discusses innovative algorithms in graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of cutting-edge algorithms in graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative algorithmic approaches in the realm of graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques in graph theory algorithm development.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel algorithms in the study of graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading-edge algorithm innovations in graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics calculations at hadron colliders') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id\nFROM SimilarArticles\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you snag me the IDs of the top 5 articles that are closely related to Quantum chromodynamics calculations at hadron colliders? I'd love to see which ones are the most similar!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics at hadron collider experiments') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider quantum chromodynamics analyses') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics studies at particle colliders') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Calculations involving quantum chromodynamics at collider experiments') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigations of quantum chromodynamics at hadron colliders') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pairs at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the IDs, titles, and abstracts of the top 5 articles related to quantum chromodynamics and photon pairs at hadron colliders?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in hadron collider photon pair production') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pairs and QCD interactions at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on QCD and photon pair events at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider experiments involving quantum chromodynamics and photon pairs') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research articles on photon pairs and QCD at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A comprehensive study of quantum chromodynamics in collider physics') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please locate the top three articles that delve deeply into quantum chromodynamics in the context of collider physics? I need their titles and abstracts!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth exploration of quantum chromodynamics related to collider experiments') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Detailed analysis of quantum chromodynamics within collider physics') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Extensive research on quantum chromodynamics in the realm of collider physics') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thorough investigation of quantum chromodynamics in the context of particle colliders') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep dive into quantum chromodynamics as applied to collider physics studies') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon production in colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 articles related to quantum chromodynamics and photon production in colliders? I need their IDs and arXiv IDs!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon emissions in particle accelerators') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Interactions of photons and quantum chromodynamics in collider experiments') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon generation and quantum chromodynamics in high-energy collisions') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics effects on photon production in colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon production mechanisms in quantum chromodynamics within colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics and hadron collider') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name\n FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title\nFROM FilteredArticles fa\nJOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I want to find the titles of the top 10 articles whose abstracts best discuss the topic of Quantum Chromodynamics and hadron collider. Please ensure these are sorted by how closely the articles match this concept.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics in particle physics and collider experiments') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of Quantum Chromodynamics and high-energy colliders') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on Quantum Chromodynamics and collider physics') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics phenomena in hadron colliders') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of Quantum Chromodynamics in hadron collider research') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical advancements in quantum computing') AS ref_vec_0\n\nSELECT \n a.arxiv_id AS arxiv_id, \n a.title AS title, \n s.name AS submitter_name, \n c.code AS category_code,\n distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN \n article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN \n categories c ON toString(ac.category_id) = toString(c.id)\nWHERE \n c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Top 5 articles about theoretical advancements in quantum computing in the 'quant-ph' category. List their arXiv IDs, titles, submitter names, and category codes.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Advances in quantum computing theory') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical progress in quantum computing') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing theoretical developments') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical innovations in quantum computing') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New theoretical insights in quantum computing') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics in collider physics') AS ref_vec_0\n\nSELECT \n a.id AS id,\n a.title AS title,\n s.name AS submitter_name,\n v.version_num AS version_num,\n distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN versions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 6, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Can you list the 5 articles most strongly related to the exploration of quantum chromodynamics in collider physics, including their titles, submitter names, version numbers, and similarity distances?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics research in particle colliders') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on quantum chromodynamics within collider experiments') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigations into quantum chromodynamics in collider physics') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring quantum chromodynamics through collider studies') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics exploration in the context of collider physics') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Recent advances in quantum computing and their implications') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT \n article_id,\n MAX(created) as latest_created\n FROM \n versions\n GROUP BY \n article_id\n)\n\nSELECT \n a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nJOIN \n versions v ON toString(a.id) = toString(v.article_id) AND v.created = lv.latest_created\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Unveil the 5 foremost articles that illuminate the dawn of quantum computing and its ripple effects.", + "external_knowledge": "The `MATCH` operator performs an approximate nearest neighbor (ANN) search, which is commonly used in vector databases to find the most similar items based on embeddings. In this context, `lembed('all-MiniLM-L6-v2')` represents a semantic model that transforms textual descriptions into high-dimensional vectors. The `k = 5` constraint specifies that the query should return the top 5 articles whose abstract embeddings closely align with the idea of \"Recent advances in quantum computing and their implications\". This operation typically uses Euclidean distance as a measure of similarity, with closer distances indicating higher similarity.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Pioneering works on the emergence of quantum computing and its effects') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) as latest_created FROM versions GROUP BY article_id\n)\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) JOIN versions v ON toString(a.id) = toString(v.article_id) AND v.created = lv.latest_created\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading articles on the onset of quantum computing and its consequences') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) as latest_created FROM versions GROUP BY article_id\n)\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) JOIN versions v ON toString(a.id) = toString(v.article_id) AND v.created = lv.latest_created\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top publications exploring the beginnings of quantum computing and its impact') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) as latest_created FROM versions GROUP BY article_id\n)\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) JOIN versions v ON toString(a.id) = toString(v.article_id) AND v.created = lv.latest_created\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Foremost papers detailing the rise of quantum computing and its ramifications') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) as latest_created FROM versions GROUP BY article_id\n)\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) JOIN versions v ON toString(a.id) = toString(v.article_id) AND v.created = lv.latest_created\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prominent studies on the advent of quantum computing and its effects') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) as latest_created FROM versions GROUP BY article_id\n)\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) JOIN versions v ON toString(a.id) = toString(v.article_id) AND v.created = lv.latest_created\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced algorithm for graph decomposition') AS ref_vec_0\n\nSELECT \n a.arxiv_id AS arxiv_id,\n a.title AS title,\n s.name AS submitter_name,\n c.code AS category_code, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 7, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "(Natural Language Question capturing all query elements)", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods for graph partitioning') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge techniques in graph theory') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph decomposition using advanced algorithms') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sophisticated approaches for graph breakdown') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel graph decomposition strategies') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, c.code AS category_code, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'title_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and collider physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN authors au ON toString(au.id) = toString(aa.author_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the titles and authors of the top three articles that are most aligned with the topic of \"Quantum chromodynamics and collider physics.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in particle physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(au.id) = toString(aa.author_id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Collider physics and quantum interactions') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(au.id) = toString(aa.author_id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum field theory in collider experiments') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(au.id) = toString(aa.author_id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-energy physics and quantum chromodynamics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(au.id) = toString(aa.author_id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on quantum chromodynamics and particle collisions') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(au.id) = toString(aa.author_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'title_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Artificial Intelligence in Healthcare') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, a.abstract_embedding, s.name AS submitter_name, ac.category_id, distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM articles a\n JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\n JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ra.title\nFROM RecentArticles ra\nWHERE ra.update_date > '2023-01-01'\nAND ra.category_id IN (\n SELECT id FROM categories WHERE code IN ('cs.AI', 'cs.LG')\n)\nORDER BY ra.distance LIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you tell me the title of the top article about \"Artificial Intelligence in Healthcare\" that was updated this year and falls under AI or Machine Learning categories?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'AI in the Healthcare Sector') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, a.abstract_embedding, s.name AS submitter_name, ac.category_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ra.title FROM RecentArticles ra WHERE ra.update_date > '2023-01-01' AND ra.category_id IN ( SELECT id FROM categories WHERE code IN ('cs.AI', 'cs.LG') ) ORDER BY ra.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Healthcare Applications of Artificial Intelligence') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, a.abstract_embedding, s.name AS submitter_name, ac.category_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ra.title FROM RecentArticles ra WHERE ra.update_date > '2023-01-01' AND ra.category_id IN ( SELECT id FROM categories WHERE code IN ('cs.AI', 'cs.LG') ) ORDER BY ra.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'AI-driven Healthcare Innovations') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, a.abstract_embedding, s.name AS submitter_name, ac.category_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ra.title FROM RecentArticles ra WHERE ra.update_date > '2023-01-01' AND ra.category_id IN ( SELECT id FROM categories WHERE code IN ('cs.AI', 'cs.LG') ) ORDER BY ra.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Machine Learning in Healthcare Systems') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, a.abstract_embedding, s.name AS submitter_name, ac.category_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ra.title FROM RecentArticles ra WHERE ra.update_date > '2023-01-01' AND ra.category_id IN ( SELECT id FROM categories WHERE code IN ('cs.AI', 'cs.LG') ) ORDER BY ra.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'AI and Machine Learning for Healthcare') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, a.abstract_embedding, s.name AS submitter_name, ac.category_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ra.title FROM RecentArticles ra WHERE ra.update_date > '2023-01-01' AND ra.category_id IN ( SELECT id FROM categories WHERE code IN ('cs.AI', 'cs.LG') ) ORDER BY ra.distance LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced quantum computational techniques') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT \n article_id,\n MAX(version_num) AS latest_version\n FROM versions\n GROUP BY article_id\n)\n\nSELECT \n a.id AS id,\n a.title AS title,\n v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n LatestVersions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you provide the IDs, titles, and latest version numbers for the top 5 articles that are most relevant to \"Advanced quantum computational techniques\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge quantum computing methods') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative techniques in quantum computation') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'State-of-the-art quantum computing strategies') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advanced methodologies') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques in quantum computing') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative techniques in quantum computing') AS ref_vec_0\n\nSELECT \n a.id AS id,\n a.title AS title,\n v.version_num AS version_num,\n v.created AS created,\n distance(a.title_embedding, ref_vec_0) AS distance\nFROM \n articles AS a\nJOIN \n versions AS v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 8, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the IDs, titles, version numbers, creation dates, and associated distances for the top 5 articles related to innovative techniques in quantum computing, limiting the results to 10 articles based on their relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge methods in quantum computing') AS ref_vec_0\n\nSELECT a.id, a.title, v.version_num, v.created, distance(a.title_embedding, ref_vec_0) AS distance FROM articles AS a JOIN versions AS v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced approaches in quantum computing') AS ref_vec_0\n\nSELECT a.id, a.title, v.version_num, v.created, distance(a.title_embedding, ref_vec_0) AS distance FROM articles AS a JOIN versions AS v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel strategies in quantum computing') AS ref_vec_0\n\nSELECT a.id, a.title, v.version_num, v.created, distance(a.title_embedding, ref_vec_0) AS distance FROM articles AS a JOIN versions AS v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods for quantum computing') AS ref_vec_0\n\nSELECT a.id, a.title, v.version_num, v.created, distance(a.title_embedding, ref_vec_0) AS distance FROM articles AS a JOIN versions AS v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pioneering techniques in quantum computing') AS ref_vec_0\n\nSELECT a.id, a.title, v.version_num, v.created, distance(a.title_embedding, ref_vec_0) AS distance FROM articles AS a JOIN versions AS v ON toString(a.id) = toString(v.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Deep learning techniques in neural networks') AS ref_vec_0\n\nSELECT sub.name, distance(art.abstract_embedding, ref_vec_0) AS distance\nFROM articles art\nJOIN submitters sub ON toString(art.submitter_id) = toString(sub.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Can you get me the names of the top 5 people who submitted articles related to deep learning and neural networks? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Neural network advancements in deep learning') AS ref_vec_0\n\nSELECT sub.name, distance(art.abstract_embedding, ref_vec_0) AS distance FROM articles art JOIN submitters sub ON toString(art.submitter_id) = toString(sub.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovations in neural networks and deep learning') AS ref_vec_0\n\nSELECT sub.name, distance(art.abstract_embedding, ref_vec_0) AS distance FROM articles art JOIN submitters sub ON toString(art.submitter_id) = toString(sub.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep learning applications in neural networks') AS ref_vec_0\n\nSELECT sub.name, distance(art.abstract_embedding, ref_vec_0) AS distance FROM articles art JOIN submitters sub ON toString(art.submitter_id) = toString(sub.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring neural networks and deep learning') AS ref_vec_0\n\nSELECT sub.name, distance(art.abstract_embedding, ref_vec_0) AS distance FROM articles art JOIN submitters sub ON toString(art.submitter_id) = toString(sub.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on deep learning and neural networks') AS ref_vec_0\n\nSELECT sub.name, distance(art.abstract_embedding, ref_vec_0) AS distance FROM articles art JOIN submitters sub ON toString(art.submitter_id) = toString(sub.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques in machine learning and AI.') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you identify the names of the five submitters who have written articles focusing on advanced techniques in machine learning and AI?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge methods in AI and machine learning.') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative approaches to machine learning and AI.') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'State-of-the-art machine learning and AI techniques.') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced machine learning strategies and artificial intelligence.') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Progressive AI and machine learning methodologies.') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics at hadron colliders') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nWHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the top 3 articles about \"Quantum chromodynamics at hadron colliders\" that were submitted by John Doe? I’d like to know their titles and how closely they match.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'QCD studies at hadron colliders') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics research in collider experiments') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on QCD at particle colliders') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring quantum chromodynamics in hadron collider experiments') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigation of QCD phenomena at hadron colliders') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques in quantum chromodynamics') AS ref_vec_0\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I need to find the IDs of the top 5 articles that are most relevant to the topic of advanced techniques in quantum chromodynamics, and these should be sorted by their closeness to the topic based on the similarity model.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics advanced methodologies') AS ref_vec_0\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge quantum chromodynamics techniques') AS ref_vec_0\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative strategies in quantum chromodynamics') AS ref_vec_0\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Progressive quantum chromodynamics methods') AS ref_vec_0\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics advanced approaches') AS ref_vec_0\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Study on quantum chromodynamics in collider physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT \n a.id AS article_id, \n aa.author_id AS author_id,\n distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM \n articles a\n JOIN \n article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 5\n),\n\nAuthorArticleCounts AS (\n SELECT \n sa.author_id AS author_id, \n COUNT(sa.article_id) AS article_count\n FROM \n SimilarArticles sa\n GROUP BY \n sa.author_id AS author_id\n)\n\nSELECT \n ath.name AS name\nFROM \n AuthorArticleCounts aac\nJOIN \n authors ath ON toString(aac.author_id) = toString(ath.id)\nORDER BY \n aac.article_count DESC;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Who are the authors leading the charge in research about quantum chromodynamics in collider physics?", + "external_knowledge": "The `MATCH` operator in vector searches performs an approximate nearest neighbor (ANN) search, retrieving the most relevant items based on a specified query. Here, it uses the `lembed` function with the 'all-MiniLM-L6-v2' model to encode the search phrase into a vector. The `k = 5` parameter means the query fetches the top five items with the smallest Euclidean distance to the search vector, which indicates the highest similarity. In this context, the embedding represents the essence of \"Study on quantum chromodynamics in collider physics,\" and the query seeks articles embodying this theme, subsequently identifying the most prolific authors in this research area.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading authors in quantum chromodynamics within collider studies') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 5\n),\n\nAuthorArticleCounts AS (\n SELECT sa.author_id, COUNT(sa.article_id) AS article_count FROM SimilarArticles sa GROUP BY sa.author_id\n)\n\nSELECT ath.name FROM AuthorArticleCounts aac JOIN authors ath ON toString(aac.author_id) = toString(ath.id) ORDER BY aac.article_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prominent researchers in quantum chromodynamics for collider physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 5\n),\n\nAuthorArticleCounts AS (\n SELECT sa.author_id, COUNT(sa.article_id) AS article_count FROM SimilarArticles sa GROUP BY sa.author_id\n)\n\nSELECT ath.name FROM AuthorArticleCounts aac JOIN authors ath ON toString(aac.author_id) = toString(ath.id) ORDER BY aac.article_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top contributors to quantum chromodynamics research in collider physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 5\n),\n\nAuthorArticleCounts AS (\n SELECT sa.author_id, COUNT(sa.article_id) AS article_count FROM SimilarArticles sa GROUP BY sa.author_id\n)\n\nSELECT ath.name FROM AuthorArticleCounts aac JOIN authors ath ON toString(aac.author_id) = toString(ath.id) ORDER BY aac.article_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Influential authors in studies of quantum chromodynamics and collider physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 5\n),\n\nAuthorArticleCounts AS (\n SELECT sa.author_id, COUNT(sa.article_id) AS article_count FROM SimilarArticles sa GROUP BY sa.author_id\n)\n\nSELECT ath.name FROM AuthorArticleCounts aac JOIN authors ath ON toString(aac.author_id) = toString(ath.id) ORDER BY aac.article_count DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Researchers advancing quantum chromodynamics in collider physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 5\n),\n\nAuthorArticleCounts AS (\n SELECT sa.author_id, COUNT(sa.article_id) AS article_count FROM SimilarArticles sa GROUP BY sa.author_id\n)\n\nSELECT ath.name FROM AuthorArticleCounts aac JOIN authors ath ON toString(aac.author_id) = toString(ath.id) ORDER BY aac.article_count DESC;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Graph algorithms and tree decompositions') AS ref_vec_0\n\nSELECT a.id, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN versions v ON toString(a.id) = toString(v.article_id)\nWHERE v.version_num > '1.0'\nAND v.created > '2022-01-01'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 6, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I need to find the IDs and version numbers of articles that have abstracts related to \"Graph algorithms and tree decompositions\". Only include articles with a version number greater than 1.0, created after January 1, 2022, and return the top 3 most relevant matches sorted by their proximity in vector space.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph theory and tree decomposition techniques') AS ref_vec_0\n\nSELECT a.id, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions v ON toString(a.id) = toString(v.article_id) WHERE v.version_num > '1.0' AND v.created > '2022-01-01'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Algorithms for graph structures and tree partitions') AS ref_vec_0\n\nSELECT a.id, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions v ON toString(a.id) = toString(v.article_id) WHERE v.version_num > '1.0' AND v.created > '2022-01-01'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tree decomposition methods in graph algorithms') AS ref_vec_0\n\nSELECT a.id, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions v ON toString(a.id) = toString(v.article_id) WHERE v.version_num > '1.0' AND v.created > '2022-01-01'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph algorithms focusing on tree structures') AS ref_vec_0\n\nSELECT a.id, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions v ON toString(a.id) = toString(v.article_id) WHERE v.version_num > '1.0' AND v.created > '2022-01-01'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Decomposition of trees in graph algorithms') AS ref_vec_0\n\nSELECT a.id, v.version_num, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions v ON toString(a.id) = toString(v.article_id) WHERE v.version_num > '1.0' AND v.created > '2022-01-01'\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of advanced machine learning techniques in data science') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles that really dive into advanced machine learning techniques in data science, and let me know who submitted them?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth analysis of machine learning methods in data science') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Comprehensive study on advanced machine learning applications in data science') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Detailed exploration of sophisticated machine learning techniques in data science') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thorough discussion on cutting-edge machine learning strategies in data science') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced machine learning techniques for data science deep dive') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and massive photon pairs') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Who are the people linked to some articles closely related to quantum chromodynamics and photon pairs, and what's the measure for their similarity?", + "external_knowledge": "In vector operations using `sqlite-lembed`, the `MATCH` operator facilitates an approximate nearest neighbor (ANN) search, identifying items with embeddings closest to a given semantic concept. The parameter `k=5` specifies that the top 5 closest matches should be returned. Vector comparisons typically use the Euclidean distance (L2 norm) as a metric for similarity, where a smaller distance indicates a higher degree of similarity. In this context, \"Quantum chromodynamics and massive photon pairs\" serves as a conceptual phrase guiding the search, likely sourced from fields related to particle physics.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pair correlations') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics interactions with photon pairs') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pair dynamics') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics with photon pairs') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics studies involving photon pairs') AS ref_vec_0\n\nSELECT s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in collider physics with photon production') AS ref_vec_0\n\nSELECT id, arxiv_id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Could you please provide the details, including IDs, arXiv IDs, titles, abstracts, and similarity distances, for the top 5 articles closely related to the topic \"Quantum chromodynamics in collider physics with photon production,\" ordered by their similarity measure?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon production in collider experiments') AS ref_vec_0\n\nSELECT id, arxiv_id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon production within quantum chromodynamics at colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Collider physics involving quantum chromodynamics and photons') AS ref_vec_0\n\nSELECT id, arxiv_id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Interactions of quantum chromodynamics with photon emission in colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on photon production in quantum chromodynamics at collider physics') AS ref_vec_0\n\nSELECT id, arxiv_id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Quantum Computing Algorithms') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nWHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the top 5 articles related to advanced quantum computing algorithms submitted by John Doe?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Computing Algorithm Advances') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge Quantum Algorithm Developments') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Latest Innovations in Quantum Computing Algorithms') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Techniques in Quantum Computing') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent Quantum Computing Algorithm Research') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics at hadron colliders with focus on massive photon pairs') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT\n a.id AS article_id,\n a.title AS title,\n aa.author_id AS author_id,\n distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM\n articles a\n JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT\n sa.title AS title,\n au.name AS author_name\nFROM\n SimilarArticles sa\nJOIN authors au ON toString(sa.author_id) = toString(au.id)\nORDER BY\n sa.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles and authors of the top 3 articles that closely relate to the exploration of quantum chromodynamics at hadron colliders focusing on massive photon pairs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics studies at hadron colliders focusing on heavy photon pairs') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, a.title, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT sa.title, au.name AS author_name FROM SimilarArticles sa JOIN authors au ON toString(sa.author_id) = toString(au.id) ORDER BY sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigations into quantum chromodynamics at hadron colliders with emphasis on large photon pairs') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, a.title, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT sa.title, au.name AS author_name FROM SimilarArticles sa JOIN authors au ON toString(sa.author_id) = toString(au.id) ORDER BY sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics at hadron colliders focusing on substantial photon pairs') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, a.title, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT sa.title, au.name AS author_name FROM SimilarArticles sa JOIN authors au ON toString(sa.author_id) = toString(au.id) ORDER BY sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics at hadron colliders with focus on large photon pairs') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, a.title, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT sa.title, au.name AS author_name FROM SimilarArticles sa JOIN authors au ON toString(sa.author_id) = toString(au.id) ORDER BY sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of quantum chromodynamics at hadron colliders concentrating on massive photon pairs') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id AS article_id, a.title, aa.author_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT sa.title, au.name AS author_name FROM SimilarArticles sa JOIN authors au ON toString(sa.author_id) = toString(au.id) ORDER BY sa.distance;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics in particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Please provide the abstracts of the top 5 articles related to quantum chromodynamics in particle physics, along with the names of the individuals who submitted these articles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on quantum chromodynamics within particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics in the field of particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigations into quantum chromodynamics in particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of quantum chromodynamics related to particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics studies in the realm of particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A new method in quantum mechanics') AS ref_vec_0\n\nSELECT ar.id, ar.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM (\n SELECT a.id, a.arxiv_id, distance\n FROM articles a\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you list the arXiv IDs and internal IDs of the top 10 articles related to a new method in quantum mechanics, authored by someone named Smith, that belong to the quantum physics category?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative approach in quantum mechanics') AS ref_vec_0\n\nSELECT ar.id, ar.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM ( SELECT a.id, a.arxiv_id, distance FROM articles a\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Smith’s novel quantum mechanics technique') AS ref_vec_0\n\nSELECT ar.id, ar.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM ( SELECT a.id, a.arxiv_id, distance FROM articles a\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum physics breakthrough method') AS ref_vec_0\n\nSELECT ar.id, ar.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM ( SELECT a.id, a.arxiv_id, distance FROM articles a\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Smith’s quantum mechanics innovation') AS ref_vec_0\n\nSELECT ar.id, ar.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM ( SELECT a.id, a.arxiv_id, distance FROM articles a\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced quantum mechanics method') AS ref_vec_0\n\nSELECT ar.id, ar.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM ( SELECT a.id, a.arxiv_id, distance FROM articles a\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'graph and algorithmic solutions') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, av.version_num, av.created, distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM articles a\n JOIN versions av ON toString(a.id) = toString(av.article_id)\n WHERE av.version_num = 'v1'\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredCategories AS (\n SELECT sa.id, sa.abstract, sa.version_num, sa.created, c.code, sa.distance\n FROM SimilarAbstracts sa\n JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id)\n JOIN categories c ON toString(ac.category_id) = toString(c.id)\n WHERE c.code IN ('cs.AI', 'cs.LG')\n),\n\nAggregateResults AS (\n SELECT fc.id, fc.abstract, fc.version_num, fc.created, fc.code, fc.distance,\n COUNT(fc.code) OVER() AS category_count,\n AVG(fc.distance) OVER() AS avg_distance\n FROM FilteredCategories fc\n)\n\nSELECT ar.id\nFROM AggregateResults ar\nORDER BY ar.avg_distance DESC\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you please find the article ID for the top 3 articles most relevant to \"graph and algorithmic solutions\" in version 1 that fall under the categories of Artificial Intelligence (cs.AI) or Machine Learning (cs.LG)? I need the one with the highest average distance!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'graph algorithms and solutions') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, av.version_num, av.created, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) WHERE av.version_num = 'v1'\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredCategories AS (\n SELECT sa.id, sa.abstract, sa.version_num, sa.created, c.code, sa.distance FROM SimilarAbstracts sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('cs.AI', 'cs.LG')\n),\n\nAggregateResults AS (\n SELECT fc.id, fc.abstract, fc.version_num, fc.created, fc.code, fc.distance, COUNT(fc.code) OVER() AS category_count, AVG(fc.distance) OVER() AS avg_distance FROM FilteredCategories fc\n)\n\nSELECT ar.id FROM AggregateResults ar ORDER BY ar.avg_distance DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'algorithmic graph solutions') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, av.version_num, av.created, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) WHERE av.version_num = 'v1'\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredCategories AS (\n SELECT sa.id, sa.abstract, sa.version_num, sa.created, c.code, sa.distance FROM SimilarAbstracts sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('cs.AI', 'cs.LG')\n),\n\nAggregateResults AS (\n SELECT fc.id, fc.abstract, fc.version_num, fc.created, fc.code, fc.distance, COUNT(fc.code) OVER() AS category_count, AVG(fc.distance) OVER() AS avg_distance FROM FilteredCategories fc\n)\n\nSELECT ar.id FROM AggregateResults ar ORDER BY ar.avg_distance DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'solutions using graphs and algorithms') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, av.version_num, av.created, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) WHERE av.version_num = 'v1'\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredCategories AS (\n SELECT sa.id, sa.abstract, sa.version_num, sa.created, c.code, sa.distance FROM SimilarAbstracts sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('cs.AI', 'cs.LG')\n),\n\nAggregateResults AS (\n SELECT fc.id, fc.abstract, fc.version_num, fc.created, fc.code, fc.distance, COUNT(fc.code) OVER() AS category_count, AVG(fc.distance) OVER() AS avg_distance FROM FilteredCategories fc\n)\n\nSELECT ar.id FROM AggregateResults ar ORDER BY ar.avg_distance DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'graph-based algorithm solutions') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, av.version_num, av.created, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) WHERE av.version_num = 'v1'\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredCategories AS (\n SELECT sa.id, sa.abstract, sa.version_num, sa.created, c.code, sa.distance FROM SimilarAbstracts sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('cs.AI', 'cs.LG')\n),\n\nAggregateResults AS (\n SELECT fc.id, fc.abstract, fc.version_num, fc.created, fc.code, fc.distance, COUNT(fc.code) OVER() AS category_count, AVG(fc.distance) OVER() AS avg_distance FROM FilteredCategories fc\n)\n\nSELECT ar.id FROM AggregateResults ar ORDER BY ar.avg_distance DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'graph and algorithmic methods') AS ref_vec_0,\n\nSimilarAbstracts AS (\n SELECT a.id, a.abstract, av.version_num, av.created, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) WHERE av.version_num = 'v1'\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredCategories AS (\n SELECT sa.id, sa.abstract, sa.version_num, sa.created, c.code, sa.distance FROM SimilarAbstracts sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('cs.AI', 'cs.LG')\n),\n\nAggregateResults AS (\n SELECT fc.id, fc.abstract, fc.version_num, fc.created, fc.code, fc.distance, COUNT(fc.code) OVER() AS category_count, AVG(fc.distance) OVER() AS avg_distance FROM FilteredCategories fc\n)\n\nSELECT ar.id FROM AggregateResults ar ORDER BY ar.avg_distance DESC LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Graph Decomposition Techniques') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.update_date\n FROM articles a\n WHERE a.update_date >= date_sub(YEAR, 1, now())\n),\n\nSimilarTitles AS (\n SELECT id, distance(articles.title_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM RecentArticles a\nJOIN SimilarTitles st ON toString(a.id) = toString(st.id)\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code IN ('CS', 'AI') \nORDER BY st.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the top ten articles from the past year that are most relevant to \"Graph Decomposition Techniques\", focusing on those categorized under Computer Science or Artificial Intelligence, and provide their titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph Partitioning Methods') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.update_date FROM articles a WHERE a.update_date >= date_sub(YEAR, 1, now())\n),\n\nSimilarTitles AS (\n SELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title FROM RecentArticles a JOIN SimilarTitles st ON toString(a.id) = toString(st.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('CS', 'AI') ORDER BY st.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Techniques for Graph Segmentation') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.update_date FROM articles a WHERE a.update_date >= date_sub(YEAR, 1, now())\n),\n\nSimilarTitles AS (\n SELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title FROM RecentArticles a JOIN SimilarTitles st ON toString(a.id) = toString(st.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('CS', 'AI') ORDER BY st.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Approaches to Graph Decomposition') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.update_date FROM articles a WHERE a.update_date >= date_sub(YEAR, 1, now())\n),\n\nSimilarTitles AS (\n SELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title FROM RecentArticles a JOIN SimilarTitles st ON toString(a.id) = toString(st.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('CS', 'AI') ORDER BY st.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph Analysis Techniques in Computer Science') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.update_date FROM articles a WHERE a.update_date >= date_sub(YEAR, 1, now())\n),\n\nSimilarTitles AS (\n SELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title FROM RecentArticles a JOIN SimilarTitles st ON toString(a.id) = toString(st.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('CS', 'AI') ORDER BY st.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Decomposition Strategies for Graphs in AI') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT a.id, a.title, a.update_date FROM articles a WHERE a.update_date >= date_sub(YEAR, 1, now())\n),\n\nSimilarTitles AS (\n SELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title FROM RecentArticles a JOIN SimilarTitles st ON toString(a.id) = toString(st.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('CS', 'AI') ORDER BY st.distance LIMIT 10;" + ], + "integration_level": 3, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques for quantum chromodynamics calculations') AS ref_vec_0\n\nSELECT \n a.id AS id, \n a.title AS title, \n s.name AS submitter_name, \n COUNT(v.id) AS total_versions, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN versions v ON toString(a.id) = toString(v.article_id)\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code IN ('quant-ph', 'hep-th')\nGROUP BY a.id, a.title, s.name\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "What are the details of the top five articles that closely resemble advanced quantum chromodynamics techniques and are categorized under quantum or high energy physics?", + "external_knowledge": "In vector search operations, the `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search. This involves comparing vector representations of text data for similarity. The `lembed('all-MiniLM-L6-v2', \"...\")` function converts the given text into a vector format using the 'all-MiniLM-L6-v2' model, which is a language model for embedding text. The `k = 5` parameter limits the results to the top 5 most similar articles. The results are based on Euclidean distance calculations, where smaller distances indicate higher similarity. This approach is beneficial for retrieving articles that share a conceptual similarity with the given topic.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods in quantum chromodynamics') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, COUNT(v.id) AS total_versions, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('quant-ph', 'hep-th') GROUP BY a.id, a.title, s.name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics advanced methodologies') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, COUNT(v.id) AS total_versions, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('quant-ph', 'hep-th') GROUP BY a.id, a.title, s.name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge quantum chromodynamics approaches') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, COUNT(v.id) AS total_versions, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('quant-ph', 'hep-th') GROUP BY a.id, a.title, s.name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sophisticated quantum chromodynamics techniques') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, COUNT(v.id) AS total_versions, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('quant-ph', 'hep-th') GROUP BY a.id, a.title, s.name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced quantum chromodynamics strategies') AS ref_vec_0\n\nSELECT a.id, a.title, s.name AS submitter_name, COUNT(v.id) AS total_versions, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN versions v ON toString(a.id) = toString(v.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code IN ('quant-ph', 'hep-th') GROUP BY a.id, a.title, s.name\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'significant findings in quantum mechanics') AS ref_vec_0\n\nSELECT \n a.arxiv_id AS arxiv_id,\n a.title AS title,\n auth.name AS author_name,\n cat.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN authors auth ON toString(aa.author_id) = toString(auth.id)\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories cat ON toString(ac.category_id) = toString(cat.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the top 5 articles that have made significant findings in quantum mechanics? I'd love to know their arXiv IDs, titles, author names, and category codes.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'breakthroughs in quantum mechanics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, auth.name AS author_name, cat.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories cat ON toString(ac.category_id) = toString(cat.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'major discoveries in quantum mechanics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, auth.name AS author_name, cat.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories cat ON toString(ac.category_id) = toString(cat.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'key advancements in quantum mechanics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, auth.name AS author_name, cat.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories cat ON toString(ac.category_id) = toString(cat.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'notable results in quantum mechanics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, auth.name AS author_name, cat.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories cat ON toString(ac.category_id) = toString(cat.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'important findings in quantum mechanics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, auth.name AS author_name, cat.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories cat ON toString(ac.category_id) = toString(cat.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Graph theory and sparse graphs') AS ref_vec_0\n\nSELECT id, title, distance(articles.title_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the IDs and titles of the top 2 articles related to 'Graph theory and sparse graphs'?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph theory and its application in sparse graphs') AS ref_vec_0\n\nSELECT id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graph analysis through graph theory') AS ref_vec_0\n\nSELECT id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring sparse graphs using graph theory') AS ref_vec_0\n\nSELECT id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph theory methods for sparse graphs') AS ref_vec_0\n\nSELECT id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph theory studies on sparse graphs') AS ref_vec_0\n\nSELECT id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 2;" + ], + "integration_level": 2, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'algorithm for graph decomposition') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT \n article_id,\n MAX(CAST(version_num AS INTEGER)) as latest_version\n FROM \n versions\n GROUP BY \n article_id\n)\n\nSELECT \n a.title AS title,\n s.name AS submitter_name,\n c.code AS category_code,\n v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n LatestVersions v ON toString(a.id) = toString(v.article_id)\nJOIN \n submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN \n article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN \n categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey, can you find me the top 5 articles that deal with algorithms for graph decomposition? I need to know their titles, who submitted them, the category codes, and their latest versions. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'graph decomposition algorithms') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(CAST(version_num AS INTEGER)) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'algorithms in graph decomposition') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(CAST(version_num AS INTEGER)) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'decomposition of graphs using algorithms') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(CAST(version_num AS INTEGER)) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'methods for graph decomposition') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(CAST(version_num AS INTEGER)) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'graph decomposition techniques') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(CAST(version_num AS INTEGER)) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, v.latest_version, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions v ON toString(a.id) = toString(v.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advancements and challenges') AS ref_vec_0\n\nSELECT id, distance(articles.title_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the IDs of the top 5 articles related to the advancements and challenges in quantum computing.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing progress and hurdles') AS ref_vec_0\n\nSELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Challenges and advancements in quantum computing') AS ref_vec_0\n\nSELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing developments and obstacles') AS ref_vec_0\n\nSELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovations and issues in quantum computing') AS ref_vec_0\n\nSELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing breakthroughs and difficulties') AS ref_vec_0\n\nSELECT id, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advances in 2023') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 4, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you dig up the titles of the top 5 articles about \"Quantum computing advances in 2023\" that fall under the 'quant-ph' category?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum computing 2023') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '2023 breakthroughs in quantum computing') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing progress in 2023') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '2023 quantum computing innovations') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in quantum computing for 2023') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advances in quantum computing and its applications') AS ref_vec_0\n\nSELECT \n a.title AS title, \n s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN \n article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN \n authors au ON toString(aa.author_id) = toString(au.id)\nWHERE \n au.name LIKE '%Einstein%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you look into those articles possibly written by someone like Einstein that dive into recent breakthroughs in quantum computing? And who sent these over?", + "external_knowledge": "The `MATCH` operator in the query performs an approximate nearest neighbor (ANN) search using vector embeddings, which allows for finding items that are similar in meaning or context. The `lembed()` function is used to create these embeddings, utilizing the 'all-MiniLM-L6-v2' model to understand text semantics. The condition `a.k = 5` specifies that only the top 5 items most similar to the specified topic should be retrieved. Similarity is typically measured using the Euclidean distance (L2 norm), where a smaller distance indicates higher similarity. In this context, searching for articles related to \"Advances in quantum computing and its applications\" implies looking for those that conceptually align with recent developments in this field.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum computing by notable scientists like Einstein') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) WHERE au.name LIKE '%Einstein%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing breakthroughs and Einstein-like authors') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) WHERE au.name LIKE '%Einstein%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative quantum computing research by authors similar to Einstein') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) WHERE au.name LIKE '%Einstein%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Explorations in quantum computing by Einstein-like figures') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) WHERE au.name LIKE '%Einstein%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advancements published by authors similar to Einstein') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) WHERE au.name LIKE '%Einstein%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advancements') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT \n article_id,\n MAX(created) as max_created\n FROM \n versions\n GROUP BY \n article_id\n)\n\nSELECT \n a.title AS title, \n s.name AS submitter_name, \n c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n RecentArticles ra ON toString(a.id) = toString(ra.article_id)\nJOIN \n submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN \n article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN \n categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 8, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles, submitter names, and category codes for the top 5 articles related to advancements in quantum computing?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovations in quantum computing') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT article_id, MAX(created) as max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN RecentArticles ra ON toString(a.id) = toString(ra.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing breakthroughs') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT article_id, MAX(created) as max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN RecentArticles ra ON toString(a.id) = toString(ra.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in quantum technology') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT article_id, MAX(created) as max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN RecentArticles ra ON toString(a.id) = toString(ra.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Progress in quantum computing') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT article_id, MAX(created) as max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN RecentArticles ra ON toString(a.id) = toString(ra.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing developments') AS ref_vec_0,\n\nRecentArticles AS (\n SELECT article_id, MAX(created) as max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN RecentArticles ra ON toString(a.id) = toString(ra.article_id) JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'In this study, we explore advanced methods for decomposing complex graphs to certify sparsity, offering efficient algorithms and insights into computational complexity.') AS ref_vec_0\n\nSELECT s.name as submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I need the names of the submitters who are responsible for the top 3 articles related to advanced methods for decomposing complex graphs, focusing on certifying sparsity and exploring efficient algorithms. Please ensure these articles are sorted by their relevance to the topic.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring innovative techniques for graph decomposition with a focus on certifying sparsity and developing efficient algorithms.') AS ref_vec_0\n\nSELECT s.name as submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced graph decomposition methods to certify sparsity and explore efficient computational algorithms.') AS ref_vec_0\n\nSELECT s.name as submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on efficient algorithms for decomposing complex graphs with an emphasis on certifying sparsity.') AS ref_vec_0\n\nSELECT s.name as submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Methods for decomposing graphs focusing on sparsity certification and algorithmic efficiency.') AS ref_vec_0\n\nSELECT s.name as submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigating advanced decomposition techniques for graphs with a focus on sparsity and efficient algorithms.') AS ref_vec_0\n\nSELECT s.name as submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of advances in quantum physics and its applications in technology') AS ref_vec_0\n\nSELECT id, arxiv_id, title, update_date, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "List the top 5 articles related to advances in quantum physics and its technological applications, along with their IDs, arXiv identifiers, titles, update dates, and similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top articles on technological advancements in quantum physics') AS ref_vec_0\n\nSELECT id, arxiv_id, title, update_date, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading research on quantum physics and its tech applications') AS ref_vec_0\n\nSELECT id, arxiv_id, title, update_date, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Breakthroughs in quantum physics for technological development') AS ref_vec_0\n\nSELECT id, arxiv_id, title, update_date, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovations in quantum physics and technology') AS ref_vec_0\n\nSELECT id, arxiv_id, title, update_date, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent advances in quantum physics and its technological uses') AS ref_vec_0\n\nSELECT id, arxiv_id, title, update_date, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Graph theory algorithms') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Which top 5 articles, akin to masterful storytellers of graph theory algorithms and residing in the realm of data structures, can you find for me?", + "external_knowledge": "The `MATCH` operator in the context of vector searches performs an approximate nearest neighbor (ANN) search to find items that are semantically similar to a specified vector representation. The `lembed()` function generates this vector using a specific model (in this case, 'all-MiniLM-L6-v2'). The parameter `k=5` indicates that the query should return the top 5 most similar items based on this vector similarity. The distance metric used is typically the Euclidean distance, where a smaller distance indicates higher similarity. In this scenario, \"Graph theory algorithms\" is represented as a vector, and articles whose titles most closely match this vector are selected, focusing on those in the 'cs.DS' category.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'graph theory storytelling in data structures') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'narrative articles on graph algorithms') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'masterful graph algorithm explanations') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top articles on graph theory in data structures') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'graph algorithms in data structure storytelling') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.DS'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and particle physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nFilteredArticles AS (\n SELECT sa.id, sa.title, sa.distance\n FROM SimilarArticles sa\n JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id)\n JOIN categories c ON toString(ac.category_id) = toString(c.id)\n WHERE c.code = 'physics'\n)\n\nSELECT fa.title, auth.name\nFROM FilteredArticles fa\nJOIN article_authors aa ON toString(fa.id) = toString(aa.article_id)\nJOIN authors auth ON toString(aa.author_id) = toString(auth.id)\nORDER BY fa.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 5 articles that are super connected to \"Quantum chromodynamics and particle physics\" and are categorized under 'physics'? I'd love to know the titles and the names of the authors of these articles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum field theory in particle physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nFilteredArticles AS (\n SELECT sa.id, sa.title, sa.distance FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\n)\n\nSELECT fa.title, auth.name FROM FilteredArticles fa JOIN article_authors aa ON toString(fa.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) ORDER BY fa.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Particle physics and quantum interactions') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nFilteredArticles AS (\n SELECT sa.id, sa.title, sa.distance FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\n)\n\nSELECT fa.title, auth.name FROM FilteredArticles fa JOIN article_authors aa ON toString(fa.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) ORDER BY fa.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced topics in particle physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nFilteredArticles AS (\n SELECT sa.id, sa.title, sa.distance FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\n)\n\nSELECT fa.title, auth.name FROM FilteredArticles fa JOIN article_authors aa ON toString(fa.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) ORDER BY fa.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics in particle physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nFilteredArticles AS (\n SELECT sa.id, sa.title, sa.distance FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\n)\n\nSELECT fa.title, auth.name FROM FilteredArticles fa JOIN article_authors aa ON toString(fa.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) ORDER BY fa.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and theoretical physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nFilteredArticles AS (\n SELECT sa.id, sa.title, sa.distance FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics'\n)\n\nSELECT fa.title, auth.name FROM FilteredArticles fa JOIN article_authors aa ON toString(fa.id) = toString(aa.article_id) JOIN authors auth ON toString(aa.author_id) = toString(auth.id) ORDER BY fa.distance LIMIT 5;" + ], + "integration_level": 3, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There's no column 'fa.title' in table 'fa': While processing WITH [-0.1582336574792862, 0.011899924837052822, 0.018621422350406647, 0.08530286699533463, -0.07802537083625793, 0.11424779146909714, 0.01120515912771225, 0.04900893568992615, -0.0073944502510130405, -0.003534844610840082, 0.01459628064185381, -0.07092037796974182, -0.1262710690498352, 0.0020041614770889282, 0.03969631716609001, 0.06112586706876755, -0.030271288007497787, 0.03110935352742672, -0.03055546246469021, -0.004539891146123409, -0.03571996092796326, -0.060412805527448654, -0.01315714605152607, -0.048788413405418396, 0.03590576350688934, 0.027264414355158806, -0.05186241492629051, 0.04314344376325607, 0.12993568181991577, -0.04242226108908653, 0.02164328843355179, 0.027787931263446808, -0.05093127489089966, 0.043682899326086044, 0.0591895692050457, 0.024700984358787537, 0.03348280116915703, -0.036634985357522964, -0.04429251700639725, -0.00643767137080431, 0.039599064737558365, 0.11106126755475998, -0.11317412555217743, 0.02982933446764946, 0.08076220005750656, -0.028820637613534927, 0.11478013545274734, 0.05668409913778305, -0.025243369862437248, -0.054810021072626114, -0.022028006613254547, 0.017062272876501083, -0.0071394797414541245, 0.0370166152715683, -0.013751586899161339, 0.0946490541100502, 0.0007794953417032957, -0.035761069506406784, 0.07075958698987961, -0.07251911610364914, -0.06102664768695831, 0.023113330826163292, -0.030016617849469185, 0.0579683817923069, 0.14837943017482758, -0.003919981420040131, 0.05238714814186096, 0.04770290106534958, 0.10610761493444443, 0.010883754119277, -0.007891589775681496, 0.01250575203448534, -0.0828763097524643, -0.020526310428977013, 0.04427928104996681, -0.02922755666077137, 0.017284605652093887, 0.018023567274212837, -0.03780012205243111, -0.01564711332321167, -0.04191591590642929, -0.10805658996105194, 0.016496269032359123, -0.054014090448617935, -0.00356312096118927, 0.07895267754793167, -0.07675300538539886, -0.054361939430236816, -0.06023934856057167, -0.02584020420908928, -0.0077050854451954365, -0.04742696136236191, 0.028425995260477066, -0.009877799078822136, 0.08753901720046997, 0.059374094009399414, 0.04371056333184242, -0.011325472965836525, 0.05214208364486694, 0.026509737595915794, 0.09289079159498215, -0.02233831211924553, -0.04395512863993645, 0.01790422573685646, 0.03585638105869293, 0.04869741201400757, 0.02650456875562668, 0.07764697074890137, -0.013448105193674564, 0.03749318793416023, 0.051083989441394806, 0.010888178832828999, 0.013479509390890598, -0.028995806351304054, -0.03707735240459442, 0.004003623034805059, 0.03026171773672104, 0.07777963578701019, -0.033929795026779175, 0.048949599266052246, 0.018788782879710197, -0.005295286886394024, -0.08515151590108871, -0.024435382336378098, -0.08050928264856339, -0.057735126465559006, -0.03267182409763336, -1.2803599263526553e-33, 0.05418888479471207, -0.04118426516652107, 0.030351683497428894, -0.01938651315867901, 0.03049718588590622, 0.010827062651515007, 0.031151412054896355, -0.028245113790035248, -0.02544277161359787, 0.004235240630805492, 0.04630092531442642, 0.03412553668022156, 0.029573621228337288, -0.05564682185649872, -0.06595925986766815, 0.03154556080698967, -0.13021817803382874, -0.006741618271917105, 0.09197532385587692, -0.002896439516916871, 0.004090839996933937, 0.02574549801647663, -0.022449465468525887, 0.006020946428179741, -0.07686284184455872, 0.013567033223807812, 0.02153625711798668, 0.04413861036300659, -0.04140225797891617, 0.04199272394180298, 0.0425850972533226, 0.04628937691450119, -0.01242860872298479, 0.011243467219173908, 0.00977715291082859, -0.020175816491246223, -0.1008308082818985, -0.006686164997518063, -0.07358445972204208, -0.023627961054444313, -0.04213770106434822, -0.031434621661901474, -0.06641510874032974, -0.03205638378858566, -0.061996664851903915, -0.018235020339488983, 0.11104772984981537, -0.08602333068847656, -0.0238095223903656, -0.014823883771896362, -0.01567908003926277, 0.010462186299264431, -0.03837336227297783, 0.01158809196203947, 0.03661375492811203, -0.005282584577798843, 0.08216948807239532, -0.040784675627946854, -0.08333936333656311, -0.03953465074300766, 0.002606382127851248, 0.07704991102218628, 0.06998653709888458, -0.010737832635641098, -0.013052450492978096, -0.06103330850601196, -0.03469792380928993, -0.02952459640800953, 0.011065305210649967, 0.010976286605000496, -0.06127657741308212, 0.07560528069734573, 0.010702497325837612, -0.018063737079501152, 0.11177259683609009, -0.027859440073370934, -0.05239858105778694, -0.11317598819732666, -0.018553026020526886, 0.06306013464927673, -0.06774874776601791, -0.053833797574043274, -0.007437463849782944, 0.023191096261143684, -0.05556889995932579, -0.0860348641872406, -0.03511384129524231, -0.015193706378340721, 0.02484971098601818, 0.009434822015464306, -0.01621408574283123, -0.06413239985704422, 0.10290482640266418, -0.02053743228316307, 0.0026179656852036715, -2.439466141050053e-33, -0.13914279639720917, 0.0372534804046154, -0.02495596557855606, 0.025616994127631187, -0.02184395119547844, 0.006950534414499998, -0.026194989681243896, -0.026073047891259193, -0.00201762979850173, 0.06009405106306076, 0.061178058385849, 0.04574533551931381, -0.012207115069031715, 0.009099355898797512, -0.022979991510510445, 0.08297377824783325, 0.016328103840351105, -0.044997964054346085, 0.011204659938812256, -0.00035675830440595746, -0.014492014423012733, 0.013244744390249252, 0.04160752519965172, 0.060735009610652924, 0.039205003529787064, 0.05968601256608963, 0.03631792962551117, -0.12672488391399384, 0.06020161136984825, 0.02129676751792431, 0.017338378354907036, -0.05568401515483856, -0.06238751858472824, 0.052525874227285385, -0.06527072191238403, -0.0632278248667717, 0.03051561303436756, 0.07099304348230362, 0.043021220713853836, -0.04347760230302811, -0.009854371659457684, 0.025277210399508476, 0.04307416081428528, -0.06445679068565369, 0.01203677523881197, 0.06723647564649582, 0.06603345274925232, -0.002370281610637903, -0.033689990639686584, 0.007560649886727333, 0.010432193987071514, 0.06969379633665085, 0.05499894171953201, 0.027887241914868355, -0.08835671842098236, 0.07297176122665405, -0.030462168157100677, 0.019637057557702065, 0.07567964494228363, -0.0002621894527692348, -0.055001892149448395, -0.06830067187547684, 0.03357952460646629, 0.028024494647979736, -0.017972033470869064, -0.03907332941889763, -0.06836406141519547, 0.02443191409111023, 0.02939542941749096, -0.1284867376089096, -0.03759509697556496, 0.03910944610834122, 0.026070086285471916, -0.007691797334700823, 0.02314397692680359, -0.005091418512165546, 0.1168619692325592, -0.11434894800186157, 0.012173609808087349, -0.138158917427063, -0.01630030758678913, 0.04631746560335159, -0.026120763272047043, -0.03092893771827221, -0.007723651360720396, -0.0529906265437603, -0.032280273735523224, 0.012584821321070194, 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toString(ac.category_id) = toString(c.id) WHERE c.code = 'physics') SELECT fa.title, auth.name FROM FilteredArticles AS fa INNER JOIN article_authors AS aa ON toString(fa.id) = toString(aa.article_id) INNER JOIN authors AS auth ON toString(aa.author_id) = toString(auth.id) ORDER BY fa.distance ASC LIMIT 5. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics in hadron colliders') AS ref_vec_0,\n\nsimilar_articles AS (\n SELECT \n a.id AS article_id, \n a.abstract AS abstract, \n distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM \n articles a\n ORDER BY distance\n LIMIT 10\n),\n\nlatest_versions AS (\n SELECT \n article_id, \n MAX(created) AS latest_version_date\n FROM \n versions \n GROUP BY \n article_id\n)\n\nSELECT \n sa.article_id AS article_id\nFROM \n similar_articles sa\nJOIN \n article_authors aa ON toString(sa.article_id) = toString(aa.article_id)\nJOIN \n authors au ON toString(aa.author_id) = toString(au.id)\nJOIN \n article_categories ac ON toString(sa.article_id) = toString(ac.article_id)\nJOIN \n categories c ON toString(ac.category_id) = toString(c.id)\nJOIN \n latest_versions lv ON toString(sa.article_id) = toString(lv.article_id)\nWHERE \n c.code = 'hep-ph' \n AND lv.latest_version_date = (\n SELECT \n MAX(lv2.latest_version_date)\n FROM \n latest_versions lv2\n JOIN \n article_categories ac2 ON toString(lv2.article_id) = toString(ac2.article_id)\n WHERE \n ac2.category_id = ac.category_id\n )\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "**\n\nCan you provide the IDs of the top 5 articles related to \"Exploration of quantum chromodynamics in hadron colliders,\" which are the latest versions in the 'hep-ph' category?\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and hadron collider exploration') AS ref_vec_0,\n\nsimilar_articles AS (\n SELECT a.id AS article_id, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nlatest_versions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT sa.article_id FROM similar_articles sa JOIN article_authors aa ON toString(sa.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(sa.article_id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN latest_versions lv ON toString(sa.article_id) = toString(lv.article_id) WHERE c.code = 'hep-ph' AND lv.latest_version_date = ( SELECT MAX(lv2.latest_version_date) FROM latest_versions lv2 JOIN article_categories ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id ) LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on quantum chromodynamics in particle colliders') AS ref_vec_0,\n\nsimilar_articles AS (\n SELECT a.id AS article_id, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nlatest_versions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT sa.article_id FROM similar_articles sa JOIN article_authors aa ON toString(sa.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(sa.article_id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN latest_versions lv ON toString(sa.article_id) = toString(lv.article_id) WHERE c.code = 'hep-ph' AND lv.latest_version_date = ( SELECT MAX(lv2.latest_version_date) FROM latest_versions lv2 JOIN article_categories ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id ) LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigating quantum chromodynamics in hadron collision environments') AS ref_vec_0,\n\nsimilar_articles AS (\n SELECT a.id AS article_id, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nlatest_versions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT sa.article_id FROM similar_articles sa JOIN article_authors aa ON toString(sa.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(sa.article_id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN latest_versions lv ON toString(sa.article_id) = toString(lv.article_id) WHERE c.code = 'hep-ph' AND lv.latest_version_date = ( SELECT MAX(lv2.latest_version_date) FROM latest_versions lv2 JOIN article_categories ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id ) LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics research in hadron collider settings') AS ref_vec_0,\n\nsimilar_articles AS (\n SELECT a.id AS article_id, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nlatest_versions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT sa.article_id FROM similar_articles sa JOIN article_authors aa ON toString(sa.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(sa.article_id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN latest_versions lv ON toString(sa.article_id) = toString(lv.article_id) WHERE c.code = 'hep-ph' AND lv.latest_version_date = ( SELECT MAX(lv2.latest_version_date) FROM latest_versions lv2 JOIN article_categories ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id ) LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics phenomena in hadron colliders') AS ref_vec_0,\n\nsimilar_articles AS (\n SELECT a.id AS article_id, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n),\n\nlatest_versions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT sa.article_id FROM similar_articles sa JOIN article_authors aa ON toString(sa.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) JOIN article_categories ac ON toString(sa.article_id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN latest_versions lv ON toString(sa.article_id) = toString(lv.article_id) WHERE c.code = 'hep-ph' AND lv.latest_version_date = ( SELECT MAX(lv2.latest_version_date) FROM latest_versions lv2 JOIN article_categories ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id ) LIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. 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0.010824530385434628, -3.546658576288804e-33, -0.1368403285741806, 0.020128406584262848, 0.026283590123057365, 0.031206319108605385, -0.00044285805779509246, -0.023782100528478622, -0.04015668109059334, -0.053257983177900314, 0.009105741046369076, 0.07150931656360626, 0.12295271456241608, 0.04259718954563141, -0.05330739542841911, -0.0029169281478971243, -0.008138268254697323, 0.0781416967511177, 0.02500193752348423, -0.030094604939222336, 0.07028147578239441, 0.005642694421112537, 0.033040668815374374, -0.007976545952260494, 0.02898545190691948, 0.042425308376550674, 0.030713925138115883, 0.055096130818128586, 0.07785877585411072, -0.14455826580524445, 0.07868244498968124, 0.01795012690126896, -0.019026709720492363, -0.0820247083902359, -0.06549365818500519, 0.04014037549495697, -0.01127353310585022, -0.023010725155472755, 0.043164875358343124, 0.05357356369495392, 0.02815278246998787, -0.09745849668979645, -0.0312106404453516, 0.04766568914055824, 0.007262649945914745, -0.003647239413112402, 0.01731245405972004, 0.07744158804416656, 0.00876607932150364, 0.02852400206029415, -0.014913764782249928, -0.027769919484853745, -0.004666812252253294, 0.0432061105966568, 0.07165145874023438, -0.012367877177894115, -0.0882149264216423, 0.044147733598947525, 0.0444265678524971, 0.013417232781648636, 0.07929515838623047, -0.020873108878731728, -0.06841853260993958, -0.09529341757297516, -0.030356300994753838, 0.05786357447504997, -0.03280797600746155, -0.013635015115141869, -0.07862070947885513, 0.09062032401561737, -0.057931218296289444, -0.08304880559444427, -0.03027220070362091, 0.040420740842819214, 0.06761050969362259, 0.00008490934123983607, -0.016034021973609924, -0.031927336007356644, 0.09704764932394028, -0.14375826716423035, 0.06635378301143646, -0.03882094845175743, -0.022611763328313828, 0.009228236973285675, -0.010250061750411987, -0.05327597260475159, -0.02806006371974945, -0.016254708170890808, -0.05296383798122406, 0.02874581515789032, -0.003657636931166053, -0.022029990330338478, -0.02527470327913761, 0.02412816509604454, 0.05277571454644203, -0.013137318193912506, 0.026417559012770653, -2.0450750426448394e-8, 0.06944073736667633, -0.04777110740542412, -0.056629814207553864, -0.035975225269794464, 0.035500481724739075, -0.03441234305500984, -0.10207443684339523, 0.055064428597688675, -0.04248104616999626, 0.08211906254291534, 0.00158948905300349, -0.011640850454568863, -0.0345902144908905, -0.009840356186032295, 0.06077117472887039, 0.04868438094854355, -0.02892434038221836, -0.03914308175444603, 0.006467408966273069, -0.06997353583574295, 0.06643503159284592, 0.01109708659350872, 0.10571310669183731, -0.01814056560397148, -0.04327859729528427, 0.02750631794333458, -0.016516191884875298, -0.02929006703197956, -0.024297131225466728, 0.04875649884343147, -0.02509963884949684, -0.019701875746250153, -0.024027639999985695, 0.04539131373167038, 0.027281953021883965, -0.06615153700113297, -0.004159267991781235, -0.08772578090429306, 0.02731732465326786, 0.007052048575133085, 0.02226714976131916, 0.04534626752138138, -0.013028783723711967, 0.1100960373878479, -0.016855891793966293, 0.04075085371732712, -0.026280539110302925, -0.00004176677612122148, 0.009722149930894375, 0.05804500728845596, -0.03639323636889458, -0.07890797406435013, -0.002108818618580699, -0.08639775961637497, -0.02278653159737587, 0.012028824537992477, 0.01097861584275961, -0.03706687316298485, -0.010519427247345448, 0.00876101478934288, 0.04564236104488373, 0.01676911488175392, 0.006139570847153664, -0.011623460799455643] AS ref_vec_0 SELECT max(latest_version_date) FROM latest_versions AS lv2 ALL INNER JOIN article_categories AS ac2 ON toString(article_id) = toString(ac2.article_id) WHERE category_id = ac.category_id', required columns: 'article_id' 'category_id' 'ac.category_id' 'latest_version_date' 'ac2.article_id' 'article_id' 'category_id' 'ac.category_id' 'latest_version_date' 'ac2.article_id', joined columns: 'ac2.article_id' 'category_id' '_part' '_part_index' '_part_uuid' '_partition_id' '_sample_factor' '_part_offset' '_part_data_version' '_row_exists' '_block_number' '_block_offset': While processing (WITH [-0.16160576045513153, 0.04129185900092125, -0.03645649179816246, 0.07400670647621155, -0.07285606861114502, 0.06531232595443726, -0.07425905019044876, 0.08101040869951248, -0.014097003266215324, -0.0378754585981369, -0.02483495883643627, -0.08050075173377991, -0.10340075194835663, -0.001994773745536804, -0.010294782929122448, 0.06060883775353432, -0.03315882012248039, 0.03637734055519104, -0.028483405709266663, -0.06135900691151619, -0.0950542762875557, -0.06468593329191208, -0.009102890267968178, -0.03599422052502632, 0.047102171927690506, 0.02394811064004898, -0.06046724319458008, 0.016468632966279984, 0.11639194935560226, -0.0018493926618248224, 0.03421475365757942, -0.03249194100499153, -0.01871044561266899, 0.05817960575222969, 0.05755160376429558, 0.02692466229200363, 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-0.03629005327820778, 9.714700870815499e-35, 0.02038027159869671, -0.031971488147974014, -0.012381946668028831, -0.02064414694905281, 0.004750168416649103, -0.012839635834097862, 0.07476802170276642, -0.0030398613307625055, -0.03347386047244072, 0.009220260195434093, 0.022210082039237022, 0.029393337666988373, 0.035632334649562836, -0.1215653121471405, -0.05186820775270462, -0.004205556586384773, -0.06474564224481583, 0.008748822845518589, 0.053126007318496704, -0.012632268480956554, 0.009401864372193813, -0.0021398153621703386, -0.042274050414562225, 0.016684044152498245, -0.0015203752554953098, 0.05510375648736954, 0.0043626693077385426, 0.07319093495607376, -0.04371488466858864, 0.01865709386765957, -0.004543969873338938, 0.07659440487623215, -0.031703703105449677, 0.03371121361851692, 0.025666819885373116, 0.04446295648813248, -0.1046912893652916, -0.013220940716564655, -0.003211671020835638, -0.007729760371148586, -0.04078329727053642, -0.002261159475892782, -0.11190000921487808, 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-0.010250061750411987, -0.05327597260475159, -0.02806006371974945, -0.016254708170890808, -0.05296383798122406, 0.02874581515789032, -0.003657636931166053, -0.022029990330338478, -0.02527470327913761, 0.02412816509604454, 0.05277571454644203, -0.013137318193912506, 0.026417559012770653, -2.0450750426448394e-8, 0.06944073736667633, -0.04777110740542412, -0.056629814207553864, -0.035975225269794464, 0.035500481724739075, -0.03441234305500984, -0.10207443684339523, 0.055064428597688675, -0.04248104616999626, 0.08211906254291534, 0.00158948905300349, -0.011640850454568863, -0.0345902144908905, -0.009840356186032295, 0.06077117472887039, 0.04868438094854355, -0.02892434038221836, -0.03914308175444603, 0.006467408966273069, -0.06997353583574295, 0.06643503159284592, 0.01109708659350872, 0.10571310669183731, -0.01814056560397148, -0.04327859729528427, 0.02750631794333458, -0.016516191884875298, -0.02929006703197956, -0.024297131225466728, 0.04875649884343147, -0.02509963884949684, -0.019701875746250153, -0.024027639999985695, 0.04539131373167038, 0.027281953021883965, -0.06615153700113297, -0.004159267991781235, -0.08772578090429306, 0.02731732465326786, 0.007052048575133085, 0.02226714976131916, 0.04534626752138138, -0.013028783723711967, 0.1100960373878479, -0.016855891793966293, 0.04075085371732712, -0.026280539110302925, -0.00004176677612122148, 0.009722149930894375, 0.05804500728845596, -0.03639323636889458, -0.07890797406435013, -0.002108818618580699, -0.08639775961637497, -0.02278653159737587, 0.012028824537992477, 0.01097861584275961, -0.03706687316298485, -0.010519427247345448, 0.00876101478934288, 0.04564236104488373, 0.01676911488175392, 0.006139570847153664, -0.011623460799455643] AS ref_vec_0 SELECT max(lv2.latest_version_date) FROM latest_versions AS lv2 INNER JOIN article_categories AS ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id) AS _subquery1: While processing latest_version_date = ((WITH [-0.16160576045513153, 0.04129185900092125, -0.03645649179816246, 0.07400670647621155, -0.07285606861114502, 0.06531232595443726, -0.07425905019044876, 0.08101040869951248, -0.014097003266215324, -0.0378754585981369, -0.02483495883643627, -0.08050075173377991, -0.10340075194835663, -0.001994773745536804, -0.010294782929122448, 0.06060883775353432, -0.03315882012248039, 0.03637734055519104, -0.028483405709266663, -0.06135900691151619, -0.0950542762875557, -0.06468593329191208, -0.009102890267968178, -0.03599422052502632, 0.047102171927690506, 0.02394811064004898, -0.06046724319458008, 0.016468632966279984, 0.11639194935560226, -0.0018493926618248224, 0.03421475365757942, -0.03249194100499153, -0.01871044561266899, 0.05817960575222969, 0.05755160376429558, 0.02692466229200363, 0.05209965258836746, -0.03229457139968872, -0.047647252678871155, -0.025620805099606514, 0.03746518865227699, 0.1027236059308052, -0.04251109063625336, 0.014812037348747253, 0.11699245870113373, 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0.02412816509604454, 0.05277571454644203, -0.013137318193912506, 0.026417559012770653, -2.0450750426448394e-8, 0.06944073736667633, -0.04777110740542412, -0.056629814207553864, -0.035975225269794464, 0.035500481724739075, -0.03441234305500984, -0.10207443684339523, 0.055064428597688675, -0.04248104616999626, 0.08211906254291534, 0.00158948905300349, -0.011640850454568863, -0.0345902144908905, -0.009840356186032295, 0.06077117472887039, 0.04868438094854355, -0.02892434038221836, -0.03914308175444603, 0.006467408966273069, -0.06997353583574295, 0.06643503159284592, 0.01109708659350872, 0.10571310669183731, -0.01814056560397148, -0.04327859729528427, 0.02750631794333458, -0.016516191884875298, -0.02929006703197956, -0.024297131225466728, 0.04875649884343147, -0.02509963884949684, -0.019701875746250153, -0.024027639999985695, 0.04539131373167038, 0.027281953021883965, -0.06615153700113297, -0.004159267991781235, -0.08772578090429306, 0.02731732465326786, 0.007052048575133085, 0.02226714976131916, 0.04534626752138138, -0.013028783723711967, 0.1100960373878479, -0.016855891793966293, 0.04075085371732712, -0.026280539110302925, -0.00004176677612122148, 0.009722149930894375, 0.05804500728845596, -0.03639323636889458, -0.07890797406435013, -0.002108818618580699, -0.08639775961637497, -0.02278653159737587, 0.012028824537992477, 0.01097861584275961, -0.03706687316298485, -0.010519427247345448, 0.00876101478934288, 0.04564236104488373, 0.01676911488175392, 0.006139570847153664, -0.011623460799455643] AS ref_vec_0 SELECT max(lv2.latest_version_date) FROM latest_versions AS lv2 INNER JOIN article_categories AS ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id) AS _subquery1): While processing (code = 'hep-ph') AND (latest_version_date = ((WITH [-0.16160576045513153, 0.04129185900092125, -0.03645649179816246, 0.07400670647621155, -0.07285606861114502, 0.06531232595443726, -0.07425905019044876, 0.08101040869951248, 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-0.0882149264216423, 0.044147733598947525, 0.0444265678524971, 0.013417232781648636, 0.07929515838623047, -0.020873108878731728, -0.06841853260993958, -0.09529341757297516, -0.030356300994753838, 0.05786357447504997, -0.03280797600746155, -0.013635015115141869, -0.07862070947885513, 0.09062032401561737, -0.057931218296289444, -0.08304880559444427, -0.03027220070362091, 0.040420740842819214, 0.06761050969362259, 0.00008490934123983607, -0.016034021973609924, -0.031927336007356644, 0.09704764932394028, -0.14375826716423035, 0.06635378301143646, -0.03882094845175743, -0.022611763328313828, 0.009228236973285675, -0.010250061750411987, -0.05327597260475159, -0.02806006371974945, -0.016254708170890808, -0.05296383798122406, 0.02874581515789032, -0.003657636931166053, -0.022029990330338478, -0.02527470327913761, 0.02412816509604454, 0.05277571454644203, -0.013137318193912506, 0.026417559012770653, -2.0450750426448394e-8, 0.06944073736667633, -0.04777110740542412, -0.056629814207553864, -0.035975225269794464, 0.035500481724739075, -0.03441234305500984, -0.10207443684339523, 0.055064428597688675, -0.04248104616999626, 0.08211906254291534, 0.00158948905300349, -0.011640850454568863, -0.0345902144908905, -0.009840356186032295, 0.06077117472887039, 0.04868438094854355, -0.02892434038221836, -0.03914308175444603, 0.006467408966273069, -0.06997353583574295, 0.06643503159284592, 0.01109708659350872, 0.10571310669183731, -0.01814056560397148, -0.04327859729528427, 0.02750631794333458, -0.016516191884875298, -0.02929006703197956, -0.024297131225466728, 0.04875649884343147, -0.02509963884949684, -0.019701875746250153, -0.024027639999985695, 0.04539131373167038, 0.027281953021883965, -0.06615153700113297, -0.004159267991781235, -0.08772578090429306, 0.02731732465326786, 0.007052048575133085, 0.02226714976131916, 0.04534626752138138, -0.013028783723711967, 0.1100960373878479, -0.016855891793966293, 0.04075085371732712, -0.026280539110302925, -0.00004176677612122148, 0.009722149930894375, 0.05804500728845596, -0.03639323636889458, -0.07890797406435013, -0.002108818618580699, -0.08639775961637497, -0.02278653159737587, 0.012028824537992477, 0.01097861584275961, -0.03706687316298485, -0.010519427247345448, 0.00876101478934288, 0.04564236104488373, 0.01676911488175392, 0.006139570847153664, -0.011623460799455643] AS ref_vec_0 SELECT max(lv2.latest_version_date) FROM latest_versions AS lv2 INNER JOIN article_categories AS ac2 ON toString(lv2.article_id) = toString(ac2.article_id) WHERE ac2.category_id = ac.category_id) AS _subquery1)). (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The study of quantum entanglement in particle physics') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nWHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Identify the top 3 articles related to quantum entanglement in particle physics within the 'quant-ph' category.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum entanglement phenomena in particle physics research') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum entanglement within the realm of particle physics') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigating quantum entanglement effects in particle physics') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum entanglement in the context of particle physics') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top articles discussing quantum entanglement in particle physics') AS ref_vec_0\n\nSELECT a.arxiv_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'quant-ph'\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics analysis and diphoton distributions') AS ref_vec_0\n\nSELECT abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you find the abstract of the article that best matches the topic of \"Quantum chromodynamics analysis and diphoton distributions\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of quantum chromodynamics and photon pair distributions') AS ref_vec_0\n\nSELECT abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics and diphoton interactions') AS ref_vec_0\n\nSELECT abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study on quantum chromodynamics and photon pair dynamics') AS ref_vec_0\n\nSELECT abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and diphoton distribution research') AS ref_vec_0\n\nSELECT abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics insights and diphoton distribution analysis') AS ref_vec_0\n\nSELECT abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 241, server response: Code: 241. DB::Exception: Memory limit (total) exceeded: would use 7.80 GiB (attempt to allocate chunk of 4235604 bytes), maximum: 7.20 GiB. (MEMORY_LIMIT_EXCEEDED) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A study on quantum mechanics in modern physics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the arXiv IDs, titles, names of the submitters, and update dates for the top 5 articles that are most relevant to the study of quantum mechanics in modern physics?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics in contemporary physics research') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Modern physics studies on quantum mechanics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum mechanics within modern physics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key quantum mechanics topics in modern physics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Influential papers on quantum mechanics in modern physics') AS ref_vec_0\n\nSELECT a.arxiv_id, a.title, s.name AS submitter_name, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 241, server response: Code: 241. DB::Exception: Memory limit (total) exceeded: would use 7.82 GiB (attempt to allocate chunk of 4235796 bytes), maximum: 7.20 GiB. (MEMORY_LIMIT_EXCEEDED) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graph characterization and decomposition') AS ref_vec_0\n\nSELECT \n a.title AS title, \n a.abstract AS abstract, \n s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the titles and abstracts of the top 5 articles related to \"Sparse graph characterization and decomposition\" along with the names of the submitters?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Characterization and decomposition of sparse graphs') AS ref_vec_0\n\nSELECT a.title, a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of sparse graph structures and decomposition techniques') AS ref_vec_0\n\nSELECT a.title, a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graph analysis and decomposition methods') AS ref_vec_0\n\nSELECT a.title, a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Decomposition strategies for sparse graphs') AS ref_vec_0\n\nSELECT a.title, a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graph properties and decomposition') AS ref_vec_0\n\nSELECT a.title, a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 241, server response: Code: 241. DB::Exception: Memory limit (total) exceeded: would use 7.82 GiB (attempt to allocate chunk of 4238156 bytes), maximum: 7.20 GiB. (MEMORY_LIMIT_EXCEEDED) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative research on quantum computing advances') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.update_date, av.version_num, ac.category_id, aa.author_id, distance(a.title_embedding, ref_vec_0) AS distance\n FROM articles a\n JOIN versions av ON toString(a.id) = toString(av.article_id)\n JOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\n JOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\n WHERE av.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = a.id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id, sa.title\nFROM SimilarArticles sa\nJOIN categories c ON toString(sa.category_id) = toString(c.id)\nWHERE sa.update_date > '2023-01-01'\nAND c.code LIKE 'cs.%'\nORDER BY sa.distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Show me the compass that points to the leading article in the realm where innovative research on quantum computing advances dances with computer science, published after the new dawn of 2023.", + "external_knowledge": "- **Vector Search Operations**: The `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search to find items that are most similar to a given concept. The `k=5` specifies that the top 5 closest articles are selected based on their title embeddings.\n- **Similarity Measurement**: The articles are ranked based on Euclidean distance (L2 norm) between their embeddings and the target concept embedding. A lower distance indicates higher similarity.\n- **Domain Knowledge**: Quantum computing advances are often associated with breakthroughs in technology and computer science, especially post-2023, reflecting a period of rapid development and innovation.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing breakthroughs in computer science') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.update_date, av.version_num, ac.category_id, aa.author_id, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) WHERE av.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = a.id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id, sa.title FROM SimilarArticles sa JOIN categories c ON toString(sa.category_id) = toString(c.id) WHERE sa.update_date > '2023-01-01' AND c.code LIKE 'cs.%' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading articles in quantum computing research') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.update_date, av.version_num, ac.category_id, aa.author_id, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) WHERE av.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = a.id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id, sa.title FROM SimilarArticles sa JOIN categories c ON toString(sa.category_id) = toString(c.id) WHERE sa.update_date > '2023-01-01' AND c.code LIKE 'cs.%' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovations in quantum computing and computer science') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.update_date, av.version_num, ac.category_id, aa.author_id, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) WHERE av.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = a.id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id, sa.title FROM SimilarArticles sa JOIN categories c ON toString(sa.category_id) = toString(c.id) WHERE sa.update_date > '2023-01-01' AND c.code LIKE 'cs.%' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pioneering research in quantum computing') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.update_date, av.version_num, ac.category_id, aa.author_id, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) WHERE av.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = a.id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id, sa.title FROM SimilarArticles sa JOIN categories c ON toString(sa.category_id) = toString(c.id) WHERE sa.update_date > '2023-01-01' AND c.code LIKE 'cs.%' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'State-of-the-art quantum computing research') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.update_date, av.version_num, ac.category_id, aa.author_id, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN versions av ON toString(a.id) = toString(av.article_id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) WHERE av.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = a.id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id, sa.title FROM SimilarArticles sa JOIN categories c ON toString(sa.category_id) = toString(c.id) WHERE sa.update_date > '2023-01-01' AND c.code LIKE 'cs.%' ORDER BY sa.distance LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. 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-0.06852883100509644, -0.03795958682894707, 0.0792553722858429, 0.009872586466372013, -0.009068608283996582, 0.04034915566444397, -1.4966095918111932e-8, 0.0154353566467762, -0.03977637365460396, 0.043294694274663925, 0.03000045195221901, 0.06027420237660408, -0.1041388213634491, 0.046521060168743134, -0.01677895151078701, -0.08801315724849701, -0.09666607528924942, -0.0045781261287629604, -0.041482631117105484, -0.023903384804725647, 0.059540919959545135, 0.10188738256692886, -0.020396169275045395, 0.04249383881688118, -0.053833067417144775, -0.04617529734969139, -0.025790994986891747, 0.01028988603502512, 0.043988145887851715, 0.045707959681749344, 0.03703492134809494, -0.0629318580031395, -0.047338880598545074, -0.021163329482078552, -0.07274129986763, 0.017030365765094757, 0.011062867939472198, -0.025270015001296997, 0.0016485147643834352, 0.06739631295204163, 0.11299066245555878, 0.017201527953147888, -0.08508969843387604, -0.04232599958777428, -0.060263942927122116, 0.028293903917074203, -0.0034338708501309156, -0.06098299100995064, 0.030729521065950394, -0.06084691733121872, 0.04312416911125183, -0.020106617361307144, -0.008689976297318935, 0.00903755147010088, -0.04962960630655289, 0.009489861316978931, 0.13330388069152832, 0.044612009078264236, 0.019542304798960686, 0.04992502182722092, -0.03579201176762581, 0.005064290016889572, 0.08348922431468964, -0.054500557482242584, -0.041086871176958084, 0.014009249396622181, 0.09837117046117783, 0.09788965433835983, -0.07049991190433502, -0.007999753579497337, 0.03444918245077133] AS ref_vec_0 SELECT max(version_num) FROM versions WHERE article_id = a.id', required columns: 'article_id' 'a.id' 'version_num', maybe you meant: 'article_id' or 'version_num': While processing (WITH [-0.07825663685798645, 0.026343543082475662, -0.029169820249080658, 0.009271903894841671, -0.0932515487074852, -0.03446836769580841, -0.04861503094434738, -0.03878500685095787, -0.048990149050951004, -0.018509116023778915, 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0.03703492134809494, -0.0629318580031395, -0.047338880598545074, -0.021163329482078552, -0.07274129986763, 0.017030365765094757, 0.011062867939472198, -0.025270015001296997, 0.0016485147643834352, 0.06739631295204163, 0.11299066245555878, 0.017201527953147888, -0.08508969843387604, -0.04232599958777428, -0.060263942927122116, 0.028293903917074203, -0.0034338708501309156, -0.06098299100995064, 0.030729521065950394, -0.06084691733121872, 0.04312416911125183, -0.020106617361307144, -0.008689976297318935, 0.00903755147010088, -0.04962960630655289, 0.009489861316978931, 0.13330388069152832, 0.044612009078264236, 0.019542304798960686, 0.04992502182722092, -0.03579201176762581, 0.005064290016889572, 0.08348922431468964, -0.054500557482242584, -0.041086871176958084, 0.014009249396622181, 0.09837117046117783, 0.09788965433835983, -0.07049991190433502, -0.007999753579497337, 0.03444918245077133] AS ref_vec_0 SELECT max(version_num) FROM versions WHERE article_id = a.id) AS _subquery2). (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A comprehensive study of algorithmic graph theory techniques.') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 5 articles that are most relevant to a comprehensive study of algorithmic graph theory techniques?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An in-depth exploration of techniques in algorithmic graph theory.') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced methods for studying algorithmic graph theory.') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Comprehensive analysis of graph theory algorithms.') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Detailed study of algorithmic approaches in graph theory.') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thorough examination of graph theory algorithm techniques.') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical study of quantum chromodynamics and hadron collisions') AS ref_vec_0\n\nSELECT \n a.id AS id, \n a.title AS title, \n au.name AS author_name, \n distance(a.abstract_embedding, ref_vec_0) AS distance \nFROM articles a \nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id) \nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the top 5 articles on \"Theoretical study of quantum chromodynamics and hadron collisions\" along with their titles and author names?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and hadron collision theoretical analysis') AS ref_vec_0\n\nSELECT a.id, a.title, au.name AS author_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of hadron collisions in quantum chromodynamics') AS ref_vec_0\n\nSELECT a.id, a.title, au.name AS author_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical analysis of QCD and hadron interactions') AS ref_vec_0\n\nSELECT a.id, a.title, au.name AS author_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics and hadronic collisions') AS ref_vec_0\n\nSELECT a.id, a.title, au.name AS author_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics in hadron collision events') AS ref_vec_0\n\nSELECT a.id, a.title, au.name AS author_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'abstract_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced quantum mechanics in modern physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 5 articles related to advanced quantum mechanics in modern physics, along with their authors' names?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Modern physics and quantum mechanics advancements') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Contemporary studies in quantum mechanics within modern physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum mechanics and modern physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced studies in quantum mechanics and its role in modern physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics advancements in the context of modern physics') AS ref_vec_0\n\nSELECT a.title, au.name, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'title_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'We describe a new algorithm for graph decompositions') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Please find the article that most closely describes a new algorithm for graph decompositions and return its ID along with the similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A novel algorithm for decomposing graphs is introduced') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Introducing a new method for graph decomposition') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An innovative approach to graph decomposition algorithms') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A breakthrough algorithm for graph decompositions') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'We present a new technique for decomposing graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel approach to graph theory and sparse graph algorithms') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID and similarity distance of the article most related to the topic of novel approaches in graph theory and sparse graph algorithms?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods in graph theory and algorithms for sparse graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New techniques in graph theory focusing on sparse graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in graph theory and sparse graph algorithm development') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge approaches to graph theory and sparse graph analysis') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel strategies in graph theory and dealing with sparse graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'This paper discusses advanced techniques in graph theory for efficient computation.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find the leading article that dives into high-level strategies in graph theory for computing tasks?", + "external_knowledge": "- The \"MATCH\" operator is used for approximate nearest neighbor (ANN) search, which retrieves items based on vector similarity.\n- The query utilizes the 'all-MiniLM-L6-v2' model to encode the semantic meaning of the text provided.\n- Vector similarity searches typically use Euclidean distance (L2 norm) as a measure, where the similarity increases as the distance decreases.\n- The query limits the search to the single most relevant article, implying a ranking based on semantic proximity.\n- The sentence provided for search encompasses advanced concepts in graph theory and computation, indicating the focus on sophisticated algorithmic techniques.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of high-level strategies in graph theory for computational tasks.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading methods in graph theory for enhancing computational efficiency.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth analysis of graph theory strategies for computing applications.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced graph theory techniques for solving computational problems.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key approaches in graph theory to optimize computational tasks.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics and photon pair production') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the article ID for the most pertinent article related to the exploration of quantum chromodynamics and photon pair production.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics exploration and photon pair generation') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of quantum chromodynamics linked to photon pair production') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigation of quantum chromodynamics and photon pairs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of photon pair creation in quantum chromodynamics') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics and photon pair processes') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Calculation of massive photon pairs production at hadron colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need the titles and similarity distances of the top 3 articles most relevant to \"Calculation of massive photon pairs production at hadron colliders\" from the database.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Production of massive photon pairs at hadron colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Massive photon pair creation in hadron collider experiments') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider photon pair production analysis') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pair production calculations in particle colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical study of photon pair generation in hadron colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical insights into quantum mechanics and its practical applications') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the top 5 articles related to theoretical insights into quantum mechanics and practical applications, and provide their IDs, arXiv IDs, and similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics theoretical perspectives and practical implementations') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics theories and their real-world applications') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Insights into quantum mechanics theories and practical uses') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical foundations of quantum mechanics and applications') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics theoretical understanding and practical application') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel approach to quantum chromodynamics computations') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 3 articles that relate to new methods in quantum chromodynamics calculations? I need their IDs, arXiv IDs, and titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative techniques in quantum chromodynamics calculations') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced methods for computing quantum chromodynamics') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New computational strategies in quantum chromodynamics') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum chromodynamics calculation methods') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge approaches to quantum chromodynamics computations') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A new algorithm for sparse graph characterization') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "What are the identities and titles of the five articles that have ventured closest to the frontier of sparse graph algorithms?", + "external_knowledge": "- The `MATCH` operator is used for performing an approximate nearest neighbor (ANN) search within vector embeddings.\n- The embedding model `'all-MiniLM-L6-v2'` is a powerful tool for creating vector representations of text, allowing semantic comparisons.\n- The `LIMIT` clause restricts the results to the top N most similar items, here specified as 5.\n- Similarity in vector searches is usually computed using measures like Euclidean distance (L2 norm), where smaller distance values indicate higher similarity.\n- Articles with abstracts closely matching the embedding of \"A new algorithm for sparse graph characterization\" are considered top contenders in that conceptual space.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'cutting-edge techniques in sparse graph algorithms') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'advancements in algorithms for sparse graphs') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'latest research on sparse graph algorithm frontier') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'innovative approaches to sparse graph algorithms') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'pioneering work in sparse graph algorithms') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics for photon pairs at hadron colliders') AS ref_vec_0,\n\nLatestVersion AS (\n SELECT \n article_id,\n MAX(created) AS latest_update\n FROM versions\n GROUP BY article_id\n)\n\nSELECT \n a.title AS title, \n a.abstract_embedding, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN LatestVersion lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Can you show me the titles and abstract embeddings of the 5 most recently updated articles relevant to \"Quantum chromodynamics for photon pairs at hadron colliders\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pair production in quantum chromodynamics at hadron colliders') AS ref_vec_0,\n\nLatestVersion AS (\n SELECT article_id, MAX(created) AS latest_update FROM versions GROUP BY article_id\n)\n\nSELECT a.title, a.abstract_embedding, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersion lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics processes involving photon pairs at hadron colliders') AS ref_vec_0,\n\nLatestVersion AS (\n SELECT article_id, MAX(created) AS latest_update FROM versions GROUP BY article_id\n)\n\nSELECT a.title, a.abstract_embedding, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersion lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Interactions of photon pairs in quantum chromodynamics at hadron colliders') AS ref_vec_0,\n\nLatestVersion AS (\n SELECT article_id, MAX(created) AS latest_update FROM versions GROUP BY article_id\n)\n\nSELECT a.title, a.abstract_embedding, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersion lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider studies of photon pairs in quantum chromodynamics') AS ref_vec_0,\n\nLatestVersion AS (\n SELECT article_id, MAX(created) AS latest_update FROM versions GROUP BY article_id\n)\n\nSELECT a.title, a.abstract_embedding, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersion lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics analysis of photon pair events at hadron colliders') AS ref_vec_0,\n\nLatestVersion AS (\n SELECT article_id, MAX(created) AS latest_update FROM versions GROUP BY article_id\n)\n\nSELECT a.title, a.abstract_embedding, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersion lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pairs') AS ref_vec_0,\n\nRecentVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date\n FROM versions\n GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles AS a\nJOIN RecentVersions AS rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Can you find a few articles that touch on Quantum chromodynamics and photon pairs and provide their IDs?", + "external_knowledge": "In this context, the `MATCH` operator performs an approximate nearest neighbor (ANN) search, using vector embeddings to find similarities between textual content. The `lembed` function generates a vector for the phrase \"Quantum chromodynamics and photon pairs\" using a specific language model ('all-MiniLM-L6-v2'). The `k=5` parameter specifies that the query should return the top 5 articles whose abstract embeddings are most similar to the generated vector. This similarity is typically measured using Euclidean distance, with a smaller distance indicating a closer match. The mechanism allows for identifying articles that are conceptually related to the given topic.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon interactions') AS ref_vec_0,\n\nRecentVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles AS a JOIN RecentVersions AS rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics with photon pair production') AS ref_vec_0,\n\nRecentVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles AS a JOIN RecentVersions AS rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'QCD and photon pairs') AS ref_vec_0,\n\nRecentVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles AS a JOIN RecentVersions AS rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics involving photons') AS ref_vec_0,\n\nRecentVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles AS a JOIN RecentVersions AS rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pair interactions in QCD') AS ref_vec_0,\n\nRecentVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles AS a JOIN RecentVersions AS rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'quantum chromodynamics and particle interactions in collider experiments') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT\n article_id,\n MAX(version_num) AS latest_version\n FROM\n versions\n GROUP BY\n article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Can you find the top 5 articles that delve into the intricate dance of quantum chromodynamics and the celestial ballet of particle interactions within collider experiments?", + "external_knowledge": "The vector search is performed using the `MATCH` operator, which facilitates approximate nearest neighbor (ANN) search to find similar items based on their vector representation. The embedding model 'all-MiniLM-L6-v2' is used to generate semantic embeddings of the text. The `k=5` parameter specifies that the search is looking to retrieve the top 5 results that are most semantically aligned with the input query \"quantum chromodynamics and particle interactions in collider experiments\". Euclidean distance is typically used to measure similarity, where closer distances imply higher similarity.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'the complexities of quantum chromodynamics and collider particle dynamics') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'quantum chromodynamics intricacies and particle ballet in colliders') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'the dance of quantum chromodynamics and particle interactions in experiments') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'exploration of particle interactions and quantum chromodynamics in collider research') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'investigating quantum chromodynamics and particle dynamics in collider settings') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) AS latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel algorithm for graph decomposition and characterization of sparse graphs') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT\n article_id,\n MAX(version_num) as latest_version\n FROM\n versions\n GROUP BY\n article_id\n)\n\nSELECT\n a.id AS id,\n a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM\n articles a\nJOIN\n LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Can you find five articles that really dive into breaking down graphs and understanding their sparse nature?", + "external_knowledge": "The `MATCH` operator in the query uses approximate nearest neighbor (ANN) search to determine which articles' abstract embeddings are most similar to the provided query embedding. The `lembed()` function generates these embeddings based on the 'all-MiniLM-L6-v2' language model. The number `k = 5` specifies that only the top 5 articles should be returned based on their similarity scores. The embeddings represent the semantic meaning of text inputs, allowing the system to find articles that are conceptually related to the provided description rather than relying on exact keyword matches.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Detailed exploration of graph theory and sparse graph analysis') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth study of graph structures and sparse graph properties') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Comprehensive breakdown of graphs focusing on sparsity') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Understanding sparse graphs through detailed graph analysis') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Insightful articles on graph decomposition and sparse graph characteristics') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advances and implications on cryptography.') AS ref_vec_0\n\nSELECT versions.version_num, versions.created, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM versions\nJOIN articles ON toString(versions.article_id) = toString(articles.id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 4, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Could you provide the version numbers and their creation dates for the top 3 most recent articles related to quantum computing advancements and cryptographic implications? The articles should be selected based on the closest semantic match to this topic.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum computing and their impact on cryptography.') AS ref_vec_0\n\nSELECT versions.version_num, versions.created, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM versions JOIN articles ON toString(versions.article_id) = toString(articles.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing breakthroughs and cryptographic consequences.') AS ref_vec_0\n\nSELECT versions.version_num, versions.created, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM versions JOIN articles ON toString(versions.article_id) = toString(articles.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Latest quantum computing innovations affecting cryptographic security.') AS ref_vec_0\n\nSELECT versions.version_num, versions.created, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM versions JOIN articles ON toString(versions.article_id) = toString(articles.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing progress and its implications for cryptography.') AS ref_vec_0\n\nSELECT versions.version_num, versions.created, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM versions JOIN articles ON toString(versions.article_id) = toString(articles.id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advances in quantum computing technology and cryptographic challenges.') AS ref_vec_0\n\nSELECT versions.version_num, versions.created, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM versions JOIN articles ON toString(versions.article_id) = toString(articles.id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced quantum chromodynamics calculation for collider processes') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please find the top 5 articles that are most relevant to advanced quantum chromodynamics calculations for collider processes? I need their titles, the names of who submitted them, and how closely they relate!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in advanced collider computations') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced calculations for collider processes in quantum chromodynamics') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics: advanced computational methods for collider physics') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Collider process calculations in advanced quantum chromodynamics') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced quantum chromodynamics for collider computations') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Graph decomposition techniques for optimizing network sparsity') AS ref_vec_0\n\nSELECT arxiv_id, title, distance(articles.title_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the arXiv IDs and titles of the top 5 articles related to graph decomposition techniques for optimizing network sparsity?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph partitioning methods for enhancing network sparsity') AS ref_vec_0\n\nSELECT arxiv_id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Techniques for graph decomposition to improve network sparsity') AS ref_vec_0\n\nSELECT arxiv_id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Optimizing sparsity in networks through graph decomposition') AS ref_vec_0\n\nSELECT arxiv_id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Methods for decomposing graphs to optimize network sparsity') AS ref_vec_0\n\nSELECT arxiv_id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Graph decomposition strategies for network sparsity optimization') AS ref_vec_0\n\nSELECT arxiv_id, title, distance(articles.title_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing advancements in algorithm efficiency') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version\n FROM versions\n GROUP BY article_id\n),\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id\nFROM SimilarArticles sa\nJOIN article_categories ac ON toString(sa.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nJOIN article_authors aa ON toString(sa.id) = toString(aa.article_id)\nJOIN authors a ON toString(aa.author_id) = toString(a.id)\nJOIN LatestVersions lv ON toString(sa.id) = toString(lv.article_id)\nJOIN versions v ON toString(lv.article_id) = toString(v.article_id) AND lv.latest_version = v.version_num\nWHERE c.code = 'cs.AI'\nAND a.name = 'John Doe'\nORDER BY sa.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the most relevant article written by John Doe in the cs.AI category that closely relates to advancements in quantum computing algorithm efficiency?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Efficiency improvements in quantum computing algorithms') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n),\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors a ON toString(aa.author_id) = toString(a.id) JOIN LatestVersions lv ON toString(sa.id) = toString(lv.article_id) JOIN versions v ON toString(lv.article_id) = toString(v.article_id) AND lv.latest_version = v.version_num WHERE c.code = 'cs.AI' AND a.name = 'John Doe' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing algorithm performance enhancement') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n),\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors a ON toString(aa.author_id) = toString(a.id) JOIN LatestVersions lv ON toString(sa.id) = toString(lv.article_id) JOIN versions v ON toString(lv.article_id) = toString(v.article_id) AND lv.latest_version = v.version_num WHERE c.code = 'cs.AI' AND a.name = 'John Doe' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in quantum computing algorithm efficiency') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n),\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors a ON toString(aa.author_id) = toString(a.id) JOIN LatestVersions lv ON toString(sa.id) = toString(lv.article_id) JOIN versions v ON toString(lv.article_id) = toString(v.article_id) AND lv.latest_version = v.version_num WHERE c.code = 'cs.AI' AND a.name = 'John Doe' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum computing algorithm speed improvements') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n),\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors a ON toString(aa.author_id) = toString(a.id) JOIN LatestVersions lv ON toString(sa.id) = toString(lv.article_id) JOIN versions v ON toString(lv.article_id) = toString(v.article_id) AND lv.latest_version = v.version_num WHERE c.code = 'cs.AI' AND a.name = 'John Doe' ORDER BY sa.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum algorithms efficiency advancements') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(version_num) as latest_version FROM versions GROUP BY article_id\n),\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.id FROM SimilarArticles sa JOIN article_categories ac ON toString(sa.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id) JOIN article_authors aa ON toString(sa.id) = toString(aa.article_id) JOIN authors a ON toString(aa.author_id) = toString(a.id) JOIN LatestVersions lv ON toString(sa.id) = toString(lv.article_id) JOIN versions v ON toString(lv.article_id) = toString(v.article_id) AND lv.latest_version = v.version_num WHERE c.code = 'cs.AI' AND a.name = 'John Doe' ORDER BY sa.distance LIMIT 1;" + ], + "integration_level": 3, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Machine learning advancements in natural language processing') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT a.id AS article_id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance \n FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT vs.article_id, s.name AS submitter_name, au.name AS author_name\nFROM VectorSearchResults vs\nJOIN submitters s ON toString(vs.article_id) = toString(s.id)\nJOIN article_authors aa ON toString(vs.article_id) = toString(aa.article_id)\nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY vs.distance;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Who are the submitters and authors of the top 5 articles on machine learning advancements in natural language processing?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top articles on NLP and machine learning breakthroughs') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT a.id AS article_id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT vs.article_id, s.name AS submitter_name, au.name AS author_name FROM VectorSearchResults vs JOIN submitters s ON toString(vs.article_id) = toString(s.id) JOIN article_authors aa ON toString(vs.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY vs.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading research in machine learning for NLP') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT a.id AS article_id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT vs.article_id, s.name AS submitter_name, au.name AS author_name FROM VectorSearchResults vs JOIN submitters s ON toString(vs.article_id) = toString(s.id) JOIN article_authors aa ON toString(vs.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY vs.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative approaches in NLP using machine learning') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT a.id AS article_id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT vs.article_id, s.name AS submitter_name, au.name AS author_name FROM VectorSearchResults vs JOIN submitters s ON toString(vs.article_id) = toString(s.id) JOIN article_authors aa ON toString(vs.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY vs.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge machine learning methods in NLP') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT a.id AS article_id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT vs.article_id, s.name AS submitter_name, au.name AS author_name FROM VectorSearchResults vs JOIN submitters s ON toString(vs.article_id) = toString(s.id) JOIN article_authors aa ON toString(vs.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY vs.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in NLP driven by machine learning') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT a.id AS article_id, a.title, a.abstract, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT vs.article_id, s.name AS submitter_name, au.name AS author_name FROM VectorSearchResults vs JOIN submitters s ON toString(vs.article_id) = toString(s.id) JOIN article_authors aa ON toString(vs.article_id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY vs.distance;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel algorithm for graph decomposition focusing on sparse graphs') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT \n article_id,\n MAX(created) AS latest_version_date\n FROM \n versions\n GROUP BY \n article_id\n)\n\nSELECT \n a.id AS id, \n a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n LatestVersions lv ON toString(a.id) = toString(lv.article_id)\nWHERE \n a.update_date = lv.latest_version_date\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey, could you find the top 5 articles that focus on a new algorithm for breaking down graphs, especially when they're sparse? I need their IDs and titles, and make sure they're the latest versions!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative method for decomposing sparse graphs') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) WHERE a.update_date = lv.latest_version_date\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge algorithm for sparse graph breakdown') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) WHERE a.update_date = lv.latest_version_date\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Latest techniques for sparse graph analysis') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) WHERE a.update_date = lv.latest_version_date\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent advancements in graph decomposition algorithms') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) WHERE a.update_date = lv.latest_version_date\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New strategies for handling sparse graph structures') AS ref_vec_0,\n\nLatestVersions AS (\n SELECT article_id, MAX(created) AS latest_version_date FROM versions GROUP BY article_id\n)\n\nSELECT a.id, a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN LatestVersions lv ON toString(a.id) = toString(lv.article_id) WHERE a.update_date = lv.latest_version_date\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative quantum research') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Please find the article that is most related to innovative quantum research and give me its title. I need the one closest match!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge quantum studies') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in quantum technology') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum research breakthroughs') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative quantum science') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum innovation studies') AS ref_vec_0\n\nSELECT a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel approach in quantum chromodynamics with improved accuracy and predictions') AS ref_vec_0,\n\nrecent_versions AS (\n SELECT article_id, MAX(created) AS max_created\n FROM versions\n GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN recent_versions rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Find the top article matching \"A novel approach in quantum chromodynamics with improved accuracy and predictions\", considering only articles with a k value of 5.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods in quantum chromodynamics with enhanced precision and forecast accuracy') AS ref_vec_0,\n\nrecent_versions AS (\n SELECT article_id, MAX(created) AS max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN recent_versions rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques in quantum chromodynamics offering better accuracy and prediction capabilities') AS ref_vec_0,\n\nrecent_versions AS (\n SELECT article_id, MAX(created) AS max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN recent_versions rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics novel strategies with improved accuracy and predictive power') AS ref_vec_0,\n\nrecent_versions AS (\n SELECT article_id, MAX(created) AS max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN recent_versions rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Breakthrough approaches in quantum chromodynamics with superior accuracy and forecasting') AS ref_vec_0,\n\nrecent_versions AS (\n SELECT article_id, MAX(created) AS max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN recent_versions rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New insights into quantum chromodynamics with refined accuracy and prediction models') AS ref_vec_0,\n\nrecent_versions AS (\n SELECT article_id, MAX(created) AS max_created FROM versions GROUP BY article_id\n)\n\nSELECT a.id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN recent_versions rv ON toString(a.id) = toString(rv.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in particle physics experiments') AS ref_vec_0\n\nSELECT \n a.abstract AS abstract, \n s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM \n articles a\nJOIN \n submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Please provide the abstracts and submitter names for the top 5 articles closely related to the topic of \"Quantum chromodynamics in particle physics experiments\".", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and its application in particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Experimental studies of quantum chromodynamics in particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigations of quantum chromodynamics in high-energy physics experiments') AS ref_vec_0\n\nSELECT a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Role of quantum chromodynamics in particle collision experiments') AS ref_vec_0\n\nSELECT a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics research in experimental particle physics') AS ref_vec_0\n\nSELECT a.abstract, s.name AS submitter_name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics calculations and collider predictions') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nJOIN article_categories ac ON toString(a.id) = toString(ac.article_id)\nJOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 8, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the titles of the 5 articles, whose abstracts are most related to \"Quantum chromodynamics calculations and collider predictions\", along with the names of the submitters, their category codes, and their similarity distances?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and high-energy physics predictions') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Calculations in quantum chromodynamics and collider physics') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics computations and collider outcomes') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics analysis and particle collider results') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-energy physics calculations related to quantum chromodynamics') AS ref_vec_0\n\nSELECT a.title, s.name AS submitter_name, c.code AS category_code, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) JOIN article_categories ac ON toString(a.id) = toString(ac.article_id) JOIN categories c ON toString(ac.category_id) = toString(c.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 502, server response: \r\n502 Bad Gateway\r\n\r\n

502 Bad Gateway

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics and particle physics') AS ref_vec_0\n\nSELECT au.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN article_authors aa ON toString(a.id) = toString(aa.article_id)\nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 15, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Who are the authors behind some top articles exploring quantum mechanics and particle physics?", + "external_knowledge": "In the context of vector operations in SQL using extensions like `sqlite-vec` and `sqlite-lembed`, the `MATCH` operator is employed for performing an approximate nearest neighbor (ANN) search. This technique is used to identify items that are most similar to a given input based on vector embeddings. The `lembed()` function generates these embeddings, which are then utilized in the search process. The parameter `k=5` is crucial as it specifies that the search should only return the top 5 most relevant or similar items, based on the Euclidean distance in the vector space, with smaller distances indicating higher similarity.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Authors of leading articles on quantum mechanics and particle physics') AS ref_vec_0\n\nSELECT au.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top writers discussing quantum mechanics and particle physics') AS ref_vec_0\n\nSELECT au.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned authors in quantum mechanics and particle physics') AS ref_vec_0\n\nSELECT au.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Influential articles on quantum mechanics and particle physics') AS ref_vec_0\n\nSELECT au.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Notable articles exploring quantum mechanics and particle physics') AS ref_vec_0\n\nSELECT au.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN article_authors aa ON toString(a.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

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nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and diphoton production in high energy physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, s.name AS submitter_name\nFROM SimilarArticles sa\nJOIN versions v ON toString(sa.id) = toString(v.article_id)\nJOIN submitters s ON toString(v.article_id) = toString(s.id)\nWHERE v.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = sa.id);", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you tell me the titles of the top 5 articles related to \"Quantum chromodynamics and diphoton production in high energy physics,\" along with the names of the submitters of their latest versions?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pair production in particle physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, s.name AS submitter_name FROM SimilarArticles sa JOIN versions v ON toString(sa.id) = toString(v.article_id) JOIN submitters s ON toString(v.article_id) = toString(s.id) WHERE v.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = sa.id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'High energy particle physics: Quantum chromodynamics and diphoton events') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, s.name AS submitter_name FROM SimilarArticles sa JOIN versions v ON toString(sa.id) = toString(v.article_id) JOIN submitters s ON toString(v.article_id) = toString(s.id) WHERE v.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = sa.id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on diphoton production and quantum chromodynamics in high energy physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, s.name AS submitter_name FROM SimilarArticles sa JOIN versions v ON toString(sa.id) = toString(v.article_id) JOIN submitters s ON toString(v.article_id) = toString(s.id) WHERE v.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = sa.id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics and photon pairs in high energy experiments') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, s.name AS submitter_name FROM SimilarArticles sa JOIN versions v ON toString(sa.id) = toString(v.article_id) JOIN submitters s ON toString(v.article_id) = toString(s.id) WHERE v.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = sa.id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics and diphoton production in high-energy physics') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT a.id, a.title, a.abstract, a.update_date, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, s.name AS submitter_name FROM SimilarArticles sa JOIN versions v ON toString(sa.id) = toString(v.article_id) JOIN submitters s ON toString(v.article_id) = toString(s.id) WHERE v.version_num = (SELECT MAX(version_num) FROM versions WHERE article_id = sa.id);" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT a.id, a.title, distance(a.title_embedding, ref_vec_0) AS distance\n FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT aa.article_id, au.name\nFROM RelevantArticles ra\nJOIN article_authors aa ON toString(ra.id) = toString(aa.article_id)\nJOIN authors au ON toString(aa.author_id) = toString(au.id)\nORDER BY ra.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 7, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the five articles most relevant to Quantum Chromodynamics and show me the names of their authors? It's really important to have this information ordered by the articles' similarity distance!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'QCD research') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT a.id, a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT aa.article_id, au.name FROM RelevantArticles ra JOIN article_authors aa ON toString(ra.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY ra.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum field theory in particle physics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT a.id, a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT aa.article_id, au.name FROM RelevantArticles ra JOIN article_authors aa ON toString(ra.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY ra.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Strong interaction studies') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT a.id, a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT aa.article_id, au.name FROM RelevantArticles ra JOIN article_authors aa ON toString(ra.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY ra.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics and particle dynamics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT a.id, a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT aa.article_id, au.name FROM RelevantArticles ra JOIN article_authors aa ON toString(ra.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY ra.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Particle physics and QCD') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT a.id, a.title, distance(a.title_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT aa.article_id, au.name FROM RelevantArticles ra JOIN article_authors aa ON toString(ra.id) = toString(aa.article_id) JOIN authors au ON toString(aa.author_id) = toString(au.id) ORDER BY ra.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "HTTP driver received HTTP status 503, server response: \r\n503 Service Temporarily Unavailable\r\n\r\n

503 Service Temporarily Unavailable

\r\n
nginx
\r\n\r\n (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);" + } +] \ No newline at end of file diff --git a/benchmark/data/results/arxiv/input_llm.json b/benchmark/data/results/arxiv/input_llm.json new file mode 100644 index 0000000..d24a428 --- /dev/null +++ b/benchmark/data/results/arxiv/input_llm.json @@ -0,0 +1,530 @@ +[ + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative algorithms in graph theory and their applications') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nWHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you tell me the titles of the five articles related to groundbreaking algorithms in graph theory that John Doe submitted?", + "external_knowledge": "The `MATCH` operator is used for approximate nearest neighbor (ANN) search within vector embeddings, which helps identify items closely related in meaning to a given query. The vector embeddings are compared using Euclidean distance (L2 norm), where smaller distances indicate greater similarity. The `k = 5` specifies that the query seeks to find the top 5 articles that best match the semantic context of \"Innovative algorithms in graph theory and their applications\", focusing on articles submitted by John Doe.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Groundbreaking graph theory algorithms') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Revolutionary methods in graph theory') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel approaches to graph theory algorithms') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced graph theory algorithm developments') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative graph theory algorithm submissions') AS ref_vec_0\n\nSELECT a.title, s.name, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator is used for approximate nearest neighbor (ANN) search within vector embeddings, which helps identify items closely related in meaning to a given query. The vector embeddings are compared using Euclidean distance (L2 norm), where smaller distances indicate greater similarity. The `k = 5` specifies that the query seeks to find the top 5 articles that best match the semantic context of \"Innovative algorithms in graph theory and their applications\", focusing on articles submitted by John Doe.\nCan you tell me the titles of the five articles related to groundbreaking algorithms in graph theory that John Doe submitted?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of machine learning techniques in natural language processing') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN submitters s ON toString(a.submitter_id) = toString(s.id)\nWHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles submitted by John Doe that dive into exploring machine learning techniques in natural language processing? I’d love to know their titles!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigating ML methods for NLP applications') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring AI techniques for processing human language') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Machine learning strategies in NLP research') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced ML approaches in natural language understanding') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods in machine learning for NLP') AS ref_vec_0\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN submitters s ON toString(a.submitter_id) = toString(s.id) WHERE s.name = 'John Doe'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the top 5 articles submitted by John Doe that dive into exploring machine learning techniques in natural language processing? I’d love to know their titles!\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A characterization of the family of sparse graphs and algorithmic solutions concerning tree decompositions') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the article that best matches the description \"A characterization of the family of sparse graphs and algorithmic solutions concerning tree decompositions\" and provide its title.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Characterization of sparse graph families and tree decomposition algorithms') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graphs characterization and tree decomposition methods') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Understanding sparse graphs and related tree decomposition techniques') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sparse graph family characterizations and algorithmic tree decomposition solutions') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Algorithmic approaches to sparse graphs and tree decomposition characterization') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the article that best matches the description \"A characterization of the family of sparse graphs and algorithmic solutions concerning tree decompositions\" and provide its title.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Perturbative quantum chromodynamics and massive photon pairs production') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the article that best relates to \"Perturbative quantum chromodynamics and massive photon pairs production\", including its ID and title?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics perturbation and production of photon pairs') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Massive photon pairs generation in perturbative quantum chromodynamics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Perturbative QCD and photon pair creation') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pair production') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Production of photon pairs through perturbative quantum chromodynamics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the article that best relates to \"Perturbative quantum chromodynamics and massive photon pairs production\", including its ID and title?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics explores the behavior of matter and energy at the smallest scales.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the IDs of the top 5 articles that are most relevant to the topic of quantum mechanics?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics studies the fundamental principles governing the micro-world.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics investigates atomic and subatomic particles and their interactions.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics involves understanding matter and energy at quantum levels.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The field of quantum mechanics deals with the behavior of particles at the quantum scale.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics is concerned with the laws of physics governing the smallest particles.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the IDs of the top 5 articles that are most relevant to the topic of quantum mechanics?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of graph decompositions and sparse graph algorithms.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you hook me up with the IDs and titles of the top 5 articles that dive into graph decompositions and sparse graph algorithms?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top articles on graph decompositions and algorithms for sparse graphs.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading studies in graph decompositions and sparse graph techniques.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth analysis of graph decomposition methods and sparse graph algorithms.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research articles focusing on graph decomposition and sparse graph strategies.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Insights into graph decomposition and algorithms for sparse graphs.') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you hook me up with the IDs and titles of the top 5 articles that dive into graph decompositions and sparse graph algorithms?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A breakthrough in artificial intelligence for solving complex problems') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id\n FROM article_categories ac\n JOIN categories c ON toString(ac.category_id) = toString(c.id)\n WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance\nFROM articles a\nJOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the titles of the top 5 articles categorized under 'Artificial Intelligence' that showcase a breakthrough in AI for solving complex problems.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative AI solutions for complex problem solving') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'AI advancements in tackling challenging issues') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Breakthrough AI methods for addressing complex challenges') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced AI techniques for solving difficult problems') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Revolutionary AI approaches to complex problem solving') AS ref_vec_0,\n\nCategoryCTE AS (\n SELECT ac.article_id FROM article_categories ac JOIN categories c ON toString(ac.category_id) = toString(c.id) WHERE c.code = 'cs.AI'\n)\n\nSELECT a.title, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a JOIN CategoryCTE cte ON toString(a.id) = toString(cte.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease find the titles of the top 5 articles categorized under 'Artificial Intelligence' that showcase a breakthrough in AI for solving complex problems.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics principles and applications in modern physics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the title and similarity score for the article most related to exploring quantum mechanics principles and applications in modern physics?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics principles and their role in modern physics applications') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Understanding quantum mechanics and its applications in today’s physics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring the principles of quantum mechanics in contemporary physics') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Principles and applications of quantum mechanics in modern scientific contexts') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Modern physics and the exploration of quantum mechanics principles') AS ref_vec_0\n\nSELECT id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the title and similarity score for the article most related to exploring quantum mechanics principles and applications in modern physics?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'This abstract discusses innovative algorithms in graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of cutting-edge algorithms in graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative algorithmic approaches in the realm of graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced techniques in graph theory algorithm development.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel algorithms in the study of graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading-edge algorithm innovations in graph theory.') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics calculations at hadron colliders') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id\nFROM SimilarArticles\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you snag me the IDs of the top 5 articles that are closely related to Quantum chromodynamics calculations at hadron colliders? I'd love to see which ones are the most similar!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics at hadron collider experiments') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider quantum chromodynamics analyses') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics studies at particle colliders') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Calculations involving quantum chromodynamics at collider experiments') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigations of quantum chromodynamics at hadron colliders') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT id FROM SimilarArticles ORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you snag me the IDs of the top 5 articles that are closely related to Quantum chromodynamics calculations at hadron colliders? I'd love to see which ones are the most similar!\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon pairs at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the IDs, titles, and abstracts of the top 5 articles related to quantum chromodynamics and photon pairs at hadron colliders?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics in hadron collider photon pair production') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pairs and QCD interactions at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Studies on QCD and photon pair events at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider experiments involving quantum chromodynamics and photon pairs') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research articles on photon pairs and QCD at hadron colliders') AS ref_vec_0\n\nSELECT id, title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat are the IDs, titles, and abstracts of the top 5 articles related to quantum chromodynamics and photon pairs at hadron colliders?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A comprehensive study of quantum chromodynamics in collider physics') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please locate the top three articles that delve deeply into quantum chromodynamics in the context of collider physics? I need their titles and abstracts!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth exploration of quantum chromodynamics related to collider experiments') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Detailed analysis of quantum chromodynamics within collider physics') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Extensive research on quantum chromodynamics in the realm of collider physics') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thorough investigation of quantum chromodynamics in the context of particle colliders') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep dive into quantum chromodynamics as applied to collider physics studies') AS ref_vec_0\n\nSELECT title, abstract, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please locate the top three articles that delve deeply into quantum chromodynamics in the context of collider physics? I need their titles and abstracts!\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon production in colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 articles related to quantum chromodynamics and photon production in colliders? I need their IDs and arXiv IDs!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics and photon emissions in particle accelerators') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Interactions of photons and quantum chromodynamics in collider experiments') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon generation and quantum chromodynamics in high-energy collisions') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics effects on photon production in colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon production mechanisms in quantum chromodynamics within colliders') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the top 5 articles related to quantum chromodynamics and photon production in colliders? I need their IDs and arXiv IDs!\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics and hadron collider') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name\n FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance\n FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title\nFROM FilteredArticles fa\nJOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I want to find the titles of the top 10 articles whose abstracts best discuss the topic of Quantum Chromodynamics and hadron collider. Please ensure these are sorted by how closely the articles match this concept.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics in particle physics and collider experiments') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of Quantum Chromodynamics and high-energy colliders') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on Quantum Chromodynamics and collider physics') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum Chromodynamics phenomena in hadron colliders') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of Quantum Chromodynamics in hadron collider research') AS ref_vec_0,\n\nSubmitterNames AS (\n SELECT s.id AS submitter_id, s.name AS submitter_name FROM submitters s\n),\n\nFilteredArticles AS (\n SELECT a.id, a.title, a.abstract, a.submitter_id, distance(a.abstract_embedding, ref_vec_0) AS distance FROM articles a\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT fa.title FROM FilteredArticles fa JOIN SubmitterNames sn ON toString(fa.submitter_id) = toString(sn.submitter_id);" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI want to find the titles of the top 10 articles whose abstracts best discuss the topic of Quantum Chromodynamics and hadron collider. Please ensure these are sorted by how closely the articles match this concept.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'We describe a new algorithm for graph decompositions') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Please find the article that most closely describes a new algorithm for graph decompositions and return its ID along with the similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A novel algorithm for decomposing graphs is introduced') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Introducing a new method for graph decomposition') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An innovative approach to graph decomposition algorithms') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A breakthrough algorithm for graph decompositions') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'We present a new technique for decomposing graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease find the article that most closely describes a new algorithm for graph decompositions and return its ID along with the similarity distance.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel approach to graph theory and sparse graph algorithms') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID and similarity distance of the article most related to the topic of novel approaches in graph theory and sparse graph algorithms?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative methods in graph theory and algorithms for sparse graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New techniques in graph theory focusing on sparse graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advancements in graph theory and sparse graph algorithm development') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge approaches to graph theory and sparse graph analysis') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Novel strategies in graph theory and dealing with sparse graphs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat is the ID and similarity distance of the article most related to the topic of novel approaches in graph theory and sparse graph algorithms?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'This paper discusses advanced techniques in graph theory for efficient computation.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find the leading article that dives into high-level strategies in graph theory for computing tasks?", + "external_knowledge": "- The \"MATCH\" operator is used for approximate nearest neighbor (ANN) search, which retrieves items based on vector similarity.\n- The query utilizes the 'all-MiniLM-L6-v2' model to encode the semantic meaning of the text provided.\n- Vector similarity searches typically use Euclidean distance (L2 norm) as a measure, where the similarity increases as the distance decreases.\n- The query limits the search to the single most relevant article, implying a ranking based on semantic proximity.\n- The sentence provided for search encompasses advanced concepts in graph theory and computation, indicating the focus on sophisticated algorithmic techniques.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of high-level strategies in graph theory for computational tasks.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading methods in graph theory for enhancing computational efficiency.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In-depth analysis of graph theory strategies for computing applications.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced graph theory techniques for solving computational problems.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key approaches in graph theory to optimize computational tasks.') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The \"MATCH\" operator is used for approximate nearest neighbor (ANN) search, which retrieves items based on vector similarity.\n- The query utilizes the 'all-MiniLM-L6-v2' model to encode the semantic meaning of the text provided.\n- Vector similarity searches typically use Euclidean distance (L2 norm) as a measure, where the similarity increases as the distance decreases.\n- The query limits the search to the single most relevant article, implying a ranking based on semantic proximity.\n- The sentence provided for search encompasses advanced concepts in graph theory and computation, indicating the focus on sophisticated algorithmic techniques.\nCan you find the leading article that dives into high-level strategies in graph theory for computing tasks?\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum chromodynamics and photon pair production') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the article ID for the most pertinent article related to the exploration of quantum chromodynamics and photon pair production.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum chromodynamics exploration and photon pair generation') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of quantum chromodynamics linked to photon pair production') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigation of quantum chromodynamics and photon pairs') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of photon pair creation in quantum chromodynamics') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on quantum chromodynamics and photon pair processes') AS ref_vec_0\n\nSELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the article ID for the most pertinent article related to the exploration of quantum chromodynamics and photon pair production.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Calculation of massive photon pairs production at hadron colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance \nFROM articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need the titles and similarity distances of the top 3 articles most relevant to \"Calculation of massive photon pairs production at hadron colliders\" from the database.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Production of massive photon pairs at hadron colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Massive photon pair creation in hadron collider experiments') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hadron collider photon pair production analysis') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Photon pair production calculations in particle colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical study of photon pair generation in hadron colliders') AS ref_vec_0\n\nSELECT title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI need the titles and similarity distances of the top 3 articles most relevant to \"Calculation of massive photon pairs production at hadron colliders\" from the database.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical insights into quantum mechanics and its practical applications') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the top 5 articles related to theoretical insights into quantum mechanics and practical applications, and provide their IDs, arXiv IDs, and similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics theoretical perspectives and practical implementations') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of quantum mechanics theories and their real-world applications') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Insights into quantum mechanics theories and practical uses') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Theoretical foundations of quantum mechanics and applications') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quantum mechanics theoretical understanding and practical application') AS ref_vec_0\n\nSELECT id, arxiv_id, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nFind the top 5 articles related to theoretical insights into quantum mechanics and practical applications, and provide their IDs, arXiv IDs, and similarity distances.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A novel approach to quantum chromodynamics computations') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 3 articles that relate to new methods in quantum chromodynamics calculations? I need their IDs, arXiv IDs, and titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative techniques in quantum chromodynamics calculations') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced methods for computing quantum chromodynamics') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New computational strategies in quantum chromodynamics') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent developments in quantum chromodynamics calculation methods') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge approaches to quantum chromodynamics computations') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the top 3 articles that relate to new methods in quantum chromodynamics calculations? I need their IDs, arXiv IDs, and titles.\n\nLet's think step by step!\n" + }, + { + "db_id": "arxiv", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A new algorithm for sparse graph characterization') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance\nFROM articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "What are the identities and titles of the five articles that have ventured closest to the frontier of sparse graph algorithms?", + "external_knowledge": "- The `MATCH` operator is used for performing an approximate nearest neighbor (ANN) search within vector embeddings.\n- The embedding model `'all-MiniLM-L6-v2'` is a powerful tool for creating vector representations of text, allowing semantic comparisons.\n- The `LIMIT` clause restricts the results to the top N most similar items, here specified as 5.\n- Similarity in vector searches is usually computed using measures like Euclidean distance (L2 norm), where smaller distance values indicate higher similarity.\n- Articles with abstracts closely matching the embedding of \"A new algorithm for sparse graph characterization\" are considered top contenders in that conceptual space.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'cutting-edge techniques in sparse graph algorithms') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'advancements in algorithms for sparse graphs') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'latest research on sparse graph algorithm frontier') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'innovative approaches to sparse graph algorithms') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'pioneering work in sparse graph algorithms') AS ref_vec_0\n\nSELECT id, arxiv_id, title, distance(articles.abstract_embedding, ref_vec_0) AS distance FROM articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE article_authors (\n `article_id` Nullable(Int64),\n `author_id` Nullable(Int64)\n);\nCREATE TABLE article_categories (\n `article_id` Nullable(Int64),\n `category_id` Nullable(Int64)\n);\nCREATE TABLE articles (\n `id` Nullable(Int64),\n `arxiv_id` Nullable(String),\n `submitter_id` Nullable(Int64),\n `title` Nullable(String),\n `comments` Nullable(String),\n `journal_ref` Nullable(String),\n `doi` Nullable(String),\n `report_no` Nullable(String),\n `license` Nullable(String),\n `abstract` Nullable(String),\n `update_date` Nullable(String),\n `title_embedding` Array(Float32),\n `comments_embedding` Array(Float32),\n `abstract_embedding` Array(Float32)\n);\nCREATE TABLE authors (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE categories (\n `id` Nullable(Int64),\n `code` String\n);\nCREATE TABLE submitters (\n `id` Nullable(Int64),\n `name` String\n);\nCREATE TABLE versions (\n `id` Nullable(Int64),\n `article_id` Int64,\n `version_num` String,\n `created` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator is used for performing an approximate nearest neighbor (ANN) search within vector embeddings.\n- The embedding model `'all-MiniLM-L6-v2'` is a powerful tool for creating vector representations of text, allowing semantic comparisons.\n- The `LIMIT` clause restricts the results to the top N most similar items, here specified as 5.\n- Similarity in vector searches is usually computed using measures like Euclidean distance (L2 norm), where smaller distance values indicate higher similarity.\n- Articles with abstracts closely matching the embedding of \"A new algorithm for sparse graph characterization\" are considered top contenders in that conceptual space.\nWhat are the identities and titles of the five articles that have ventured closest to the frontier of sparse graph algorithms?\n\nLet's think step by step!\n" + } +] \ No newline at end of file diff --git a/benchmark/data/results/bird/candidate_sql.json b/benchmark/data/results/bird/candidate_sql.json new file mode 100644 index 0000000..805aa27 --- /dev/null +++ b/benchmark/data/results/bird/candidate_sql.json @@ -0,0 +1,1005 @@ +[ + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'a prestigious award in sports') AS ref_vec_0,\n\nPlayerAwards AS (\n SELECT playerID, award, year, pos, distance(awards_players.note_embedding, ref_vec_0) AS distance\n FROM awards_players\n ORDER BY distance\n LIMIT 5\n),\n\nAllStarStats AS (\n SELECT pa.playerID, pa.award, pa.year, pa.pos, distance, \n ast.games_played, ast.points, ast.assists, ast.rebounds\n FROM PlayerAwards pa\n JOIN player_allstar ast ON toString(pa.playerID) = toString(ast.playerID) AND pa.year = ast.season_id\n WHERE ast.points > 500\n),\n\nTeamPerformance AS (\n SELECT ast.playerID, ast.award, ast.year, ast.pos, ast.distance,\n t.tmID, t.won, t.lost, (t.won - t.lost) AS win_diff\n FROM AllStarStats ast\n JOIN players_teams pt ON toString(ast.playerID) = toString(pt.playerID) AND ast.year = pt.year\n JOIN teams t ON toString(pt.tmID) = toString(t.tmID) AND pt.year = t.year\n WHERE t.playoff = 'Y' AND win_diff > 20\n)\n\nSELECT DISTINCT playerID\nFROM TeamPerformance\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Who is the player associated with a top award in sports, having extraordinary gameplay stats in a playoff-winning team?", + "external_knowledge": "Vector searches in this query utilize embeddings to compare text-based concepts for similarity. The `MATCH` operator of SQLite-vec performs an approximate nearest neighbor search, often using Euclidean distance (L2 norm) to quantify similarity between vectors. The `k=5` parameter specifies that the operation should identify the top 5 most similar entries to the phrase \"a prestigious award in sports,\" prioritizing those with the smallest distance. In this context, the closer the distance, the more semantically similar the award is to the described prestigious concept in sports.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'a prestigious award in sports') AS ref_vec_0,\n\nPlayerAwards AS (\n SELECT playerID, award, year, pos, distance(awards_players.note_embedding, ref_vec_0) AS distance\n FROM awards_players\n ORDER BY distance\n LIMIT 5\n),\n\nAllStarStats AS (\n SELECT pa.playerID, pa.award, pa.year, pa.pos, distance, \n ast.games_played, ast.points, ast.assists, ast.rebounds\n FROM PlayerAwards pa\n JOIN player_allstar ast ON toString(pa.playerID) = toString(ast.playerID) AND pa.year = ast.season_id\n WHERE ast.points > 500\n),\n\nTeamPerformance AS (\n SELECT ast.playerID, ast.award, ast.year, ast.pos, ast.distance,\n t.tmID, t.won, t.lost, (t.won - t.lost) AS win_diff\n FROM AllStarStats ast\n JOIN players_teams pt ON toString(ast.playerID) = toString(pt.playerID) AND ast.year = pt.year\n JOIN teams t ON toString(pt.tmID) = toString(t.tmID) AND pt.year = t.year\n WHERE t.playoff = 'Y' AND win_diff > 20\n)\n\nSELECT DISTINCT playerID\nFROM TeamPerformance\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 3, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'distance' 'playerID' while processing query: 'WITH [-0.02895197831094265, 0.11998282372951508, -0.06583737581968307, -0.08423794060945511, -0.029052074998617172, 0.06445211917161942, 0.08339203894138336, 0.07684434950351715, 0.039437029510736465, 0.07198866456747055, -0.09243229031562805, -0.031008202582597733, -0.0020472912583500147, 0.07392452657222748, -0.06168341264128685, 0.04220116138458252, 0.02271507866680622, -0.0036943256855010986, -0.031472183763980865, -0.08710640668869019, -0.017903532832860947, 0.021372660994529724, 0.02717444859445095, 0.003318694420158863, -0.04846135526895523, -0.03778637945652008, -0.0176578089594841, 0.05208049714565277, -0.04992882162332535, -0.035376183688640594, -0.04490312933921814, -0.06987164169549942, 0.06315741688013077, 0.06870530545711517, -0.10526008158922195, 0.08156908303499222, 0.013138429261744022, 0.009504804387688637, -0.0007013261783868074, 0.009223071858286858, 0.011580593883991241, 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0.06453999131917953, -0.08879005908966064, -0.0026509719900786877, -0.007007244974374771, -0.0826655775308609, -0.04343775287270546, -0.07181915640830994, -0.024857021868228912, 0.14274507761001587, 0.05913916602730751, -0.008286100812256336, 0.0790431946516037, 0.06390827149152756, -0.07670553028583527, 0.004113791044801474, 0.05332208052277565, -0.009475764818489552, 0.05332329124212265, 0.012510647997260094, 0.054614223539829254, -0.01292173657566309, 0.014451331458985806, -0.020324213430285454, 0.0010495028691366315, -0.08128572255373001, -0.03425033763051033, 0.03446345403790474, 0.03382100909948349, -0.03015327639877796, -0.038135454058647156, -0.024324025958776474, 0.005042825825512409, -0.046627312898635864, -0.036630235612392426, -0.09194312989711761, 0.08409711718559265, 0.002998620504513383, -0.022571014240384102, -0.075813889503479, -0.10079406946897507, -0.023369954898953438, -0.02040841430425644, 0.1109628900885582, -0.004548302385956049, 0.05210079997777939, 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-0.05797945708036423, 0.053236424922943115, 0.10254958271980286, -0.046728987246751785, -0.05939890071749687, -0.016966847702860832, 0.0369984395802021, -0.0006258598295971751, -0.051944345235824585, 0.0008817125344648957, 0.0021109499502927065, 0.07140003144741058, 0.006629009731113911, 0.05220721662044525, -0.1091475635766983, 0.0027160849422216415, -0.006517201662063599, -0.02093709632754326, 0.015827298164367676, 0.020050695165991783, -0.08863923698663712, 0.12988130748271942, 0.04096970334649086, -0.009495735168457031, 0.025637352839112282, 0.006529080215841532, -0.15380114316940308, -0.023688029497861862, -0.013001522049307823, 0.060877725481987, 0.005898342002183199, 0.07059314101934433, -0.09326943755149841, 0.0695081502199173, 0.013557414524257183, 0.08847472071647644, 0.011288626119494438, 0.052180808037519455, -0.06047658249735832, 0.01957160048186779, -0.033166199922561646, 0.007594413124024868, 0.03147087246179581, -0.016532812267541885, 0.0790088027715683, 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0.03892276808619499, -0.05672905966639519, -0.04431432485580444, 0.04524155333638191, -0.014226370491087437, 0.00028407436911948025, -0.014945006929337978, -0.00535529525950551, 0.047601692378520966, -0.005883508827537298, 0.005112248007208109, 0.06624563038349152, -0.05372515693306923, 0.027006074786186218, 0.013507475145161152, 0.02248067781329155, 0.011574744246900082, -0.06302328407764435, -0.03908656910061836, 0.003305916441604495, -0.04287194833159447, -0.06676264852285385, -0.06539550423622131, -0.08221779018640518, 0.042237356305122375, -0.09013485163450241, 0.04935527220368385, 0.05216760188341141] AS ref_vec_0, PlayerAwards AS (WITH [-0.02895197831094265, 0.11998282372951508, -0.06583737581968307, -0.08423794060945511, -0.029052074998617172, 0.06445211917161942, 0.08339203894138336, 0.07684434950351715, 0.039437029510736465, 0.07198866456747055, -0.09243229031562805, -0.031008202582597733, -0.0020472912583500147, 0.07392452657222748, -0.06168341264128685, 0.04220116138458252, 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-0.04287194833159447, -0.06676264852285385, -0.06539550423622131, -0.08221779018640518, 0.042237356305122375, -0.09013485163450241, 0.04935527220368385, 0.05216760188341141] AS ref_vec_0 SELECT playerID, award, year, pos, distance(awards_players.note_embedding, ref_vec_0) AS distance FROM awards_players ORDER BY distance ASC LIMIT 5), AllStarStats AS (WITH [-0.02895197831094265, 0.11998282372951508, -0.06583737581968307, -0.08423794060945511, -0.029052074998617172, 0.06445211917161942, 0.08339203894138336, 0.07684434950351715, 0.039437029510736465, 0.07198866456747055, -0.09243229031562805, -0.031008202582597733, -0.0020472912583500147, 0.07392452657222748, -0.06168341264128685, 0.04220116138458252, 0.02271507866680622, -0.0036943256855010986, -0.031472183763980865, -0.08710640668869019, -0.017903532832860947, 0.021372660994529724, 0.02717444859445095, 0.003318694420158863, -0.04846135526895523, -0.03778637945652008, -0.0176578089594841, 0.05208049714565277, -0.04992882162332535, 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-0.02826680988073349, -0.047216422855854034, -0.008420494385063648, -0.08226366341114044, 0.03469235822558403, 0.02248099446296692, 0.012838383205235004, 0.03668427839875221, 0.058450937271118164, -0.06306035816669464, 0.04567617550492287, 0.0719594955444336, 0.03892276808619499, -0.05672905966639519, -0.04431432485580444, 0.04524155333638191, -0.014226370491087437, 0.00028407436911948025, -0.014945006929337978, -0.00535529525950551, 0.047601692378520966, -0.005883508827537298, 0.005112248007208109, 0.06624563038349152, -0.05372515693306923, 0.027006074786186218, 0.013507475145161152, 0.02248067781329155, 0.011574744246900082, -0.06302328407764435, -0.03908656910061836, 0.003305916441604495, -0.04287194833159447, -0.06676264852285385, -0.06539550423622131, -0.08221779018640518, 0.042237356305122375, -0.09013485163450241, 0.04935527220368385, 0.05216760188341141] AS ref_vec_0 SELECT pa.playerID, pa.award, pa.year, pa.pos, distance, ast.games_played, ast.points, ast.assists, ast.rebounds FROM PlayerAwards AS pa INNER JOIN player_allstar AS ast ON (toString(pa.playerID) = toString(ast.playerID)) AND (pa.year = ast.season_id) WHERE ast.points > 500), TeamPerformance AS (WITH [-0.02895197831094265, 0.11998282372951508, -0.06583737581968307, -0.08423794060945511, -0.029052074998617172, 0.06445211917161942, 0.08339203894138336, 0.07684434950351715, 0.039437029510736465, 0.07198866456747055, -0.09243229031562805, -0.031008202582597733, -0.0020472912583500147, 0.07392452657222748, -0.06168341264128685, 0.04220116138458252, 0.02271507866680622, -0.0036943256855010986, -0.031472183763980865, -0.08710640668869019, -0.017903532832860947, 0.021372660994529724, 0.02717444859445095, 0.003318694420158863, -0.04846135526895523, -0.03778637945652008, -0.0176578089594841, 0.05208049714565277, -0.04992882162332535, -0.035376183688640594, -0.04490312933921814, -0.06987164169549942, 0.06315741688013077, 0.06870530545711517, -0.10526008158922195, 0.08156908303499222, 0.013138429261744022, 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-0.010627956129610538, 0.07051892578601837, 0.01151640247553587, -0.03871554881334305, -0.05797945708036423, 0.053236424922943115, 0.10254958271980286, -0.046728987246751785, -0.05939890071749687, -0.016966847702860832, 0.0369984395802021, -0.0006258598295971751, -0.051944345235824585, 0.0008817125344648957, 0.0021109499502927065, 0.07140003144741058, 0.006629009731113911, 0.05220721662044525, -0.1091475635766983, 0.0027160849422216415, -0.006517201662063599, -0.02093709632754326, 0.015827298164367676, 0.020050695165991783, -0.08863923698663712, 0.12988130748271942, 0.04096970334649086, -0.009495735168457031, 0.025637352839112282, 0.006529080215841532, -0.15380114316940308, -0.023688029497861862, -0.013001522049307823, 0.060877725481987, 0.005898342002183199, 0.07059314101934433, -0.09326943755149841, 0.0695081502199173, 0.013557414524257183, 0.08847472071647644, 0.011288626119494438, 0.052180808037519455, -0.06047658249735832, 0.01957160048186779, -0.033166199922561646, 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0.058450937271118164, -0.06306035816669464, 0.04567617550492287, 0.0719594955444336, 0.03892276808619499, -0.05672905966639519, -0.04431432485580444, 0.04524155333638191, -0.014226370491087437, 0.00028407436911948025, -0.014945006929337978, -0.00535529525950551, 0.047601692378520966, -0.005883508827537298, 0.005112248007208109, 0.06624563038349152, -0.05372515693306923, 0.027006074786186218, 0.013507475145161152, 0.02248067781329155, 0.011574744246900082, -0.06302328407764435, -0.03908656910061836, 0.003305916441604495, -0.04287194833159447, -0.06676264852285385, -0.06539550423622131, -0.08221779018640518, 0.042237356305122375, -0.09013485163450241, 0.04935527220368385, 0.05216760188341141] AS ref_vec_0 SELECT ast.playerID, ast.award, ast.year, ast.pos, ast.distance, t.tmID, t.won, t.lost, t.won - t.lost AS win_diff FROM AllStarStats AS ast INNER JOIN players_teams AS pt ON (toString(ast.playerID) = toString(pt.playerID)) AND (ast.year = pt.year) INNER JOIN teams AS t ON (toString(pt.tmID) = toString(t.tmID)) AND (pt.year = t.year) WHERE (t.playoff = 'Y') AND (win_diff > 20)) SELECT DISTINCT playerID FROM TeamPerformance ORDER BY distance ASC LIMIT 1', required columns: 'playerID' 'distance' 'playerID' 'distance'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding player performance in 2023') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top 3 players who had outstanding performances in 2023 and let me know what awards they got and when?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding player performance in 2023') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'example note content') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the top 5 players who have received awards with notes similar to \"example note content,\" and provide the awards and years they were received.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'example note content') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'achievement in sports') AS ref_vec_0\n\nSELECT playerID, award, distance(awards_players.note_embedding, ref_vec_0) AS distance \nFROM awards_players\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top player who's got an award for achievement in sports? I just need their player ID and the award they received.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'achievement in sports') AS ref_vec_0\n\nSELECT playerID, award, distance(awards_players.note_embedding, ref_vec_0) AS distance \nFROM awards_players\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'MVP award for outstanding performance') AS ref_vec_0,\n\nAwardedPlayers AS (\n SELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.award AS award,\n distance(ap.note_embedding, ref_vec_0) AS distance\n FROM \n awards_players ap\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.distance AS distance\nFROM \n AwardedPlayers ap\nJOIN \n player_allstar pa ON toString(ap.playerID) = toString(pa.playerID) AND ap.year = pa.season_id\nORDER BY \n ap.distance;", + "sql_result_column_count": 3, + "sql_result_rows_count": 6, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Who are the top 10 players recognized for MVP awards due to outstanding performance? Provide their ID, year, and similarity distance, considering their all-star participation, and order them by their relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'MVP award for outstanding performance') AS ref_vec_0,\n\nAwardedPlayers AS (\n SELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.award AS award,\n distance(ap.note_embedding, ref_vec_0) AS distance\n FROM \n awards_players ap\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.distance AS distance\nFROM \n AwardedPlayers ap\nJOIN \n player_allstar pa ON toString(ap.playerID) = toString(pa.playerID) AND ap.year = pa.season_id\nORDER BY \n ap.distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in championship games') AS ref_vec_0\n\nSELECT p.fullGivenName, ap.award, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Who are the top 5 players recognized for outstanding performance in championship games, and what awards did they receive?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in championship games') AS ref_vec_0\n\nSELECT p.fullGivenName, ap.award, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional performance in the final game') AS ref_vec_0\n\nSELECT playerID, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Could you list the IDs of the top 5 players who were noted for their exceptional performance in the final game?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional performance in the final game') AS ref_vec_0\n\nSELECT playerID, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH HighIntelligenceStudents AS (\n SELECT student_id, intelligence, f_name, l_name\n FROM student\n WHERE intelligence > 130\n)\n\nSELECT c.name AS course_name\nFROM HighIntelligenceStudents his\nJOIN registration r ON his.student_id = r.student_id\nJOIN course c ON r.course_id = c.course_id\nWHERE c.diff > 3 \n AND his.student_id IN (\n SELECT student_id \n FROM student \n WHERE type_embedding MATCH lembed('all-MiniLM-L6-v2', \"Scholar\") AND k = 5\n )\nORDER BY c.credit DESC\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the names of the top 10 courses, with a difficulty greater than 3, that are registered by students who have an intelligence score above 130 and are among the top 5 scholars?", + "external_knowledge": "", + "sql_candidate": [ + "WITH HighIntelligenceStudents AS (\n SELECT student_id, intelligence, f_name, l_name\n FROM student\n WHERE intelligence > 130\n)\n\nSELECT c.name AS course_name\nFROM HighIntelligenceStudents his\nJOIN registration r ON his.student_id = r.student_id\nJOIN course c ON r.course_id = c.course_id\nWHERE c.diff > 3 \n AND his.student_id IN (\n SELECT student_id \n FROM student \n WHERE type_embedding MATCH lembed('all-MiniLM-L6-v2', \"Scholar\") AND k = 5\n )\nORDER BY c.credit DESC\nLIMIT 10;" + ], + "execution_status": "exception", + "error_message": "歧义错误: 在多表查询中发现无别名的向量搜索列 'type_embedding'。请为该列表明表别名。", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The player exhibited exceptional performance') AS ref_vec_0\n\nSELECT p.fullGivenName, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Who are the five players that shine brightly with exceptional prowess and have stood out as stars in their field?", + "external_knowledge": "The `MATCH` operator with the `lembed()` function performs an approximate nearest neighbor search, which is used to find the most similar items based on vector embeddings. The `k=5` parameter specifies that the query should return the top 5 results. The embeddings are processed using Euclidean distance, where smaller distances imply higher similarity. The \"all-MiniLM-L6-v2\" model is designed to capture semantic meanings in vector space, allowing for nuanced comparison of textual descriptions such as \"exceptional performance.\"", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The player exhibited exceptional performance') AS ref_vec_0\n\nSELECT p.fullGivenName, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);" + }, + { + "db_id": "cs_semester", + "sql": "SELECT s.student_id\nFROM student s\nJOIN registration r ON toString(s.student_id) = toString(r.student_id)\nJOIN course c ON toString(r.course_id) = toString(c.course_id)\nWHERE s.intelligence > (\n SELECT AVG(intelligence)\n FROM student\n)\nAND s.gpa > 3.5\nAND c.diff > 3\nORDER BY s.intelligence DESC\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Who are the top five students with impressive intelligence and high GPA involved in challenging courses?", + "external_knowledge": "While this query doesn't involve vector operations, it focuses on filtering based on quantitative measures of intelligence and GPA, alongside course difficulty. Typically, in queries using vectors, operations might include similarity searches where the MATCH operator is used for approximate nearest neighbor searches. The \"k=N\" parameter limits the number of results based on similarity, with vectors compared by Euclidean distance. However, these are not applicable in the current SQL query context.", + "sql_candidate": [ + "SELECT s.student_id\nFROM student s\nJOIN registration r ON toString(s.student_id) = toString(r.student_id)\nJOIN course c ON toString(r.course_id) = toString(c.course_id)\nWHERE s.intelligence > (\n SELECT AVG(intelligence)\n FROM student\n)\nAND s.gpa > 3.5\nAND c.diff > 3\nORDER BY s.intelligence DESC\nLIMIT 5;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Stanford University') AS ref_vec_0\n\nSELECT prof_id, distance(prof.graduate_from_embedding, ref_vec_0) AS distance \nFROM prof\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the professor who graduated from an institution most similar to Stanford University and provide their unique identifier.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Stanford University') AS ref_vec_0\n\nSELECT prof_id, distance(prof.graduate_from_embedding, ref_vec_0) AS distance \nFROM prof\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'PGS') AS ref_vec_0\n\nSELECT student_id, type, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify and list the student IDs and types for the top three postgraduate students according to their vector similarity classification.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'PGS') AS ref_vec_0\n\nSELECT student_id, type, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'RPG player') AS ref_vec_0\n\nSELECT student_id, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Which student most closely relates to being an RPG player? Provide their ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'RPG player') AS ref_vec_0\n\nSELECT student_id, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'RPG characteristics and similar academic focus') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top institutions in the USA') AS ref_vec_1,\n\nstudent_filtered AS (\n SELECT\n *,\n distance(type_embedding, ref_vec_0) AS distance\n FROM student\n WHERE type_embedding MATCH lembed('all-MiniLM-L6-v2', 'RPG characteristics AND similar academic focus')\n ORDER BY distance\n LIMIT 5\n),\n\nprof_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_1) AS distance\n FROM prof\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarStudents AS (\n SELECT \n student_id, \n f_name, \n l_name, \n gpa, \n distance\n FROM student_filtered AS student\n),\n\nSimilarProfs AS (\n SELECT \n prof_id, \n first_name, \n last_name, \n popularity, \n distance\n FROM prof_filtered AS prof\n)\n\nSELECT \n AVG(SimilarStudents.gpa) AS average_gpa\nFROM \n SimilarStudents\nJOIN \n RA ON toString(SimilarStudents.student_id) = toString(RA.student_id)\nJOIN \n SimilarProfs ON toString(RA.prof_id) = toString(SimilarProfs.prof_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I want to find the average GPA of the top 5 students who exhibit characteristics similar to RPG concepts and focus on similar academic areas. These students should be working with the top 5 professors who graduated from institutions akin to leading universities in the USA. Could you provide this average GPA, please?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'RPG characteristics and similar academic focus') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top institutions in the USA') AS ref_vec_1,\n\nstudent_filtered AS (\n SELECT\n *,\n distance(type_embedding, ref_vec_0) AS distance\n FROM student\n WHERE type_embedding MATCH lembed('all-MiniLM-L6-v2', 'RPG characteristics AND similar academic focus')\n ORDER BY distance\n LIMIT 5\n),\n\nprof_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_1) AS distance\n FROM prof\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarStudents AS (\n SELECT \n student_id, \n f_name, \n l_name, \n gpa, \n distance\n FROM student_filtered AS student\n),\n\nSimilarProfs AS (\n SELECT \n prof_id, \n first_name, \n last_name, \n popularity, \n distance\n FROM prof_filtered AS prof\n)\n\nSELECT \n AVG(SimilarStudents.gpa) AS average_gpa\nFROM \n SimilarStudents\nJOIN \n RA ON toString(SimilarStudents.student_id) = toString(RA.student_id)\nJOIN \n SimilarProfs ON toString(RA.prof_id) = toString(SimilarProfs.prof_id);" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17056 ('MATCH') (line 10, col 26): MATCH [0.05218606814742088, 0.004804207012057304, -0.04950569197535515, -0.051674168556928635, -0.012943391688168049, -0.00763988122344017, 0.047267306596040726. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'high salary for research assistants') AS ref_vec_0\n\nSELECT student_id, capability, distance(RA.salary_embedding, ref_vec_0) AS distance\nFROM RA\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the student IDs, their capabilities, and the similarity distances for the top 5 research assistants associated with high salaries?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'high salary for research assistants') AS ref_vec_0\n\nSELECT student_id, capability, distance(RA.salary_embedding, ref_vec_0) AS distance\nFROM RA\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Harvurd Univ') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Scolar') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n WHERE popularity > 70\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(type_embedding, ref_vec_1) AS distance\n FROM student\n WHERE intelligence > 120\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.first_name AS professor_name,\n s.f_name AS student_name\nFROM p_filtered AS p\nJOIN\n RA r ON toString(p.prof_id) = toString(r.prof_id)\nJOIN s_filtered AS s ON toString(r.student_id) = toString(s.student_id)\nORDER BY \n p.popularity DESC, s.intelligence DESC\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Seek the brightest sparks in the academic galaxy: Who are the 10 shining stars, professors, and scholars, emerging from the esteemed halls akin to \"Harvurd Univ\", with professors basking in their glories above 70 in popularity, and scholars whose minds illuminate beyond 120 in intelligence?", + "external_knowledge": "The query leverages vector operations for approximate nearest neighbor (ANN) searches using the MATCH operator, which finds entities similar to specified concepts based on embeddings. The `k=5` clause indicates selecting the top 5 entities with the closest match for both professors and students. The embeddings are handled by the `lembed()` function using the model 'all-MiniLM-L6-v2', which assesses similarity based on Euclidean distance, where smaller distances indicate higher similarity. In this context, \"Harvurd Univ\" is associated with prestigious institutions, and \"Scholar\" signifies individuals dedicated to academic pursuits.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Harvurd Univ') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Scolar') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n WHERE popularity > 70\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(type_embedding, ref_vec_1) AS distance\n FROM student\n WHERE intelligence > 120\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.first_name AS professor_name,\n s.f_name AS student_name\nFROM p_filtered AS p\nJOIN\n RA r ON toString(p.prof_id) = toString(r.prof_id)\nJOIN s_filtered AS s ON toString(r.student_id) = toString(s.student_id)\nORDER BY \n p.popularity DESC, s.intelligence DESC\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Harvard University') AS ref_vec_0,\n\nHighSalaryRAs AS (\n SELECT student_id, prof_id\n FROM RA\n WHERE salary = 'high'\n),\n\nSimilarProfessors AS (\n SELECT prof_id, graduate_from, distance(prof.graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT s.f_name || ' ' || s.l_name AS student_name\nFROM student s\nJOIN HighSalaryRAs r ON toString(s.student_id) = toString(r.student_id)\nJOIN SimilarProfessors p ON toString(r.prof_id) = toString(p.prof_id)\nWHERE s.type = 'RA'\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of up to 10 Research Assistants with high salaries whose supervising professors graduated from one of the top 3 universities most similar to Harvard University?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Harvard University') AS ref_vec_0,\n\nHighSalaryRAs AS (\n SELECT student_id, prof_id\n FROM RA\n WHERE salary = 'high'\n),\n\nSimilarProfessors AS (\n SELECT prof_id, graduate_from, distance(prof.graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT s.f_name || ' ' || s.l_name AS student_name\nFROM student s\nJOIN HighSalaryRAs r ON toString(s.student_id) = toString(r.student_id)\nJOIN SimilarProfessors p ON toString(r.prof_id) = toString(p.prof_id)\nWHERE s.type = 'RA'\nLIMIT 10;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Beijing Polytechnic') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'high') AS ref_vec_1,\n\nprof_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n\n ORDER BY distance\n LIMIT 5\n),\n\nRA_filtered AS (\n SELECT\n *,\n distance(salary_embedding, ref_vec_1) AS distance\n FROM RA\n\n ORDER BY distance\n LIMIT 5\n),\n\nProfGraduates AS (\n SELECT \n prof_id,\n first_name,\n last_name,\n distance\n FROM prof_filtered AS prof\n)\n\nSELECT p.first_name || ' ' || p.last_name AS professor_name\nFROM ProfGraduates p\nJOIN RA_filtered AS RA p.prof_id = RA.prof_id\nORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the professor who graduated from a place like Beijing Polytechnic and is tied to a high-paying research assistant gig? I just need their name!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Beijing Polytechnic') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'high') AS ref_vec_1,\n\nprof_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n\n ORDER BY distance\n LIMIT 5\n),\n\nRA_filtered AS (\n SELECT\n *,\n distance(salary_embedding, ref_vec_1) AS distance\n FROM RA\n\n ORDER BY distance\n LIMIT 5\n),\n\nProfGraduates AS (\n SELECT \n prof_id,\n first_name,\n last_name,\n distance\n FROM prof_filtered AS prof\n)\n\nSELECT p.first_name || ' ' || p.last_name AS professor_name\nFROM ProfGraduates p\nJOIN RA_filtered AS RA p.prof_id = RA.prof_id\nORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17496 ('p') (line 36, col 24): p.prof_id = RA.prof_id\nORDER BY p.distance\nLIMIT 1\n FORMAT Native. Expected one of: FINAL, SAMPLE, USING, ON, end of query. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A brewery located in Boston, MA known for unique craft beers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An IPA with rich flavors and high ABV') AS ref_vec_1,\n\nbreweries_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n WHERE beers_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'An IPA with rich flavors AND high ABV')\n ORDER BY distance\n LIMIT 10\n),\n\nSimilarBreweries AS (\n SELECT id, name, distance\n FROM breweries_filtered AS breweries BY distance\n),\n\nFilteredBeers AS (\n SELECT b.id, b.brewery_id, b.abv, b.ibu, b.name, b.style, b.ounces, b.distance\n FROM b_filtered AS b\n JOIN SimilarBreweries sb ON toString(b.brewery_id) = toString(sb.id)\n ORDER BY b.distance\n)\n\nSELECT AVG(fb.abv) AS avg_abv\nFROM FilteredBeers fb;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you find the top 5 breweries that are based in Boston and are known for their unique craft beers? Then, from those breweries, grab me 10 IPAs that are rich in flavor and have a high ABV. I'd love to know the average ABV of those beers.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A brewery located in Boston, MA known for unique craft beers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An IPA with rich flavors and high ABV') AS ref_vec_1,\n\nbreweries_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n WHERE beers_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'An IPA with rich flavors AND high ABV')\n ORDER BY distance\n LIMIT 10\n),\n\nSimilarBreweries AS (\n SELECT id, name, distance\n FROM breweries_filtered AS breweries BY distance\n),\n\nFilteredBeers AS (\n SELECT b.id, b.brewery_id, b.abv, b.ibu, b.name, b.style, b.ounces, b.distance\n FROM b_filtered AS b\n JOIN SimilarBreweries sb ON toString(b.brewery_id) = toString(sb.id)\n ORDER BY b.distance\n)\n\nSELECT AVG(fb.abv) AS avg_abv\nFROM FilteredBeers fb;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17220 ('MATCH') (line 20, col 39): MATCH [-0.027335727587342262, -0.05739806592464447, -0.021972553804516792, -0.0006302543915808201, -0.10924375057220459, 0.018465252593159676, 0.042284697294235. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'brewery located in Portland, OR') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'India Pale Ale') AS ref_vec_1,\n\nbreweries_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbr_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 5\n),\n\nBreweryCTE AS (\n SELECT id, name, distance\n FROM breweries_filtered AS breweries\n)\n\nSELECT b.name AS brewery_name, br.name AS beer_name\nFROM BreweryCTE b\nJOIN br_filtered AS br ON toString(b.id) = toString(br.brewery_id)\nORDER BY br.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of the 5 breweries located in Portland, OR, along with the names of their top 5 India Pale Ale beers, ordered by similarity?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'brewery located in Portland, OR') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'India Pale Ale') AS ref_vec_1,\n\nbreweries_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbr_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 5\n),\n\nBreweryCTE AS (\n SELECT id, name, distance\n FROM breweries_filtered AS breweries\n)\n\nSELECT b.name AS brewery_name, br.name AS beer_name\nFROM BreweryCTE b\nJOIN br_filtered AS br ON toString(b.id) = toString(br.brewery_id)\nORDER BY br.distance;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A brewery located in Portland, OR specializing in craft beers') AS ref_vec_0\n\nSELECT id, name, distance(breweries.breweries_description_embedding, ref_vec_0) AS distance\nFROM breweries\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the top 5 breweries located in Portland, OR specializing in craft beers. Provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A brewery located in Portland, OR specializing in craft beers') AS ref_vec_0\n\nSELECT id, name, distance(breweries.breweries_description_embedding, ref_vec_0) AS distance\nFROM breweries\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'M Chinnaswamy Stadium is a venue located in the city with ID 1.') AS ref_vec_0\n\nSELECT Venue_Name, distance(Venue.Venue_description_embedding, ref_vec_0) AS distance\nFROM Venue\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the venue whose description most closely resembles the characteristics of \"M Chinnaswamy Stadium is a venue located in the city with ID 1\" and provide its name.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'M Chinnaswamy Stadium is a venue located in the city with ID 1.') AS ref_vec_0\n\nSELECT Venue_Name, distance(Venue.Venue_description_embedding, ref_vec_0) AS distance\nFROM Venue\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional batsman known for his aggressive style and consistency.') AS ref_vec_0\n\nSELECT Player_Id, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Who is the player known for being an exceptional batsman with an aggressive style and consistency, and can you provide their unique identifier?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional batsman known for his aggressive style and consistency.') AS ref_vec_0\n\nSELECT Player_Id, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An American Pale Ale with rich malt flavor') AS ref_vec_0,\n\nfiltered_breweries AS (\n SELECT id, name\n FROM breweries\n WHERE state = 'MN'\n)\n\nSELECT b.name AS beer_name, br.name AS brewery_name, distance(b.beers_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN filtered_breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Uncover the top 5 enchanting brews from Minnesotan beer gardens that echo the tale of a rich malt melody found in an American Pale Ale.", + "external_knowledge": "The query utilizes vector search capabilities provided by sqlite-vec and sqlite-lembed extensions. The `MATCH` operator is a mechanism for performing an approximate nearest neighbor (ANN) search, which is used to identify items with similar characteristics or descriptions. Here, the `lembed(all-MiniLM-L6-v2, \"An American Pale Ale with rich malt flavor\")` function creates an embedding vector from the given description and compares it against existing beer description embeddings. By specifying `k = 5`, the query aims to find the top 5 beers closely aligning with the given description. The similarity is measured by Euclidean distance, and results with smaller distances are considered more similar, thus ranked higher.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An American Pale Ale with rich malt flavor') AS ref_vec_0,\n\nfiltered_breweries AS (\n SELECT id, name\n FROM breweries\n WHERE state = 'MN'\n)\n\nSELECT b.name AS beer_name, br.name AS brewery_name, distance(b.beers_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN filtered_breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned brewery located in Denver, CO') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A popular American IPA with a high IBU score brewed at brewery in Denver') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbe_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 10\n),\n\nbrewery_matches AS (\n SELECT b.id as brewery_id, b.name, b.city, b.state, distance as brewery_distance\n FROM b_filtered AS b\n),\n\nbeer_matches AS (\n SELECT br.brewery_id, be.id as beer_id, be.name, be.style, be.abv, be.ibu, be.ounces, distance as beer_distance\n FROM be_filtered AS be\n JOIN brewery_matches br ON toString(be.brewery_id) = toString(br.brewery_id)\n)\n\nSELECT bm.beer_id\nFROM beer_matches bm\nORDER BY bm.beer_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Please identify the single beer that is most closely associated with being a popular American IPA with a high IBU score brewed at a brewery located in Denver, CO. Could you return its ID for me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned brewery located in Denver, CO') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A popular American IPA with a high IBU score brewed at brewery in Denver') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbe_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 10\n),\n\nbrewery_matches AS (\n SELECT b.id as brewery_id, b.name, b.city, b.state, distance as brewery_distance\n FROM b_filtered AS b\n),\n\nbeer_matches AS (\n SELECT br.brewery_id, be.id as beer_id, be.name, be.style, be.abv, be.ibu, be.ounces, distance as beer_distance\n FROM be_filtered AS be\n JOIN brewery_matches br ON toString(be.brewery_id) = toString(br.brewery_id)\n)\n\nSELECT bm.beer_id\nFROM beer_matches bm\nORDER BY bm.beer_distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan city with a rich cricketing history') AS ref_vec_0,\n\nCityMatch AS (\n SELECT City_Id, distance(City.City_description_embedding, ref_vec_0) AS distance\n FROM City\n ORDER BY distance\n LIMIT 1\n),\n\nVenueMatch AS (\n SELECT Venue_Id\n FROM Venue v\n JOIN CityMatch cm ON toString(v.City_Id) = toString(cm.City_Id)\n)\n\nSELECT p.Player_Name\nFROM Match m\nJOIN VenueMatch vm ON toString(m.Venue_Id) = toString(vm.Venue_Id)\nJOIN Player p ON toString(m.Man_of_the_Match) = toString(p.Player_Id)\nWHERE m.Match_Id IN (\n SELECT Match_Id\n FROM Match\n WHERE Venue_Id = vm.Venue_Id\n)\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please identify the top player recognized as the \"Man of the Match\" from a game held in a busy metropolitan city famous for its cricketing past? I need to get their name urgently!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan city with a rich cricketing history') AS ref_vec_0,\n\nCityMatch AS (\n SELECT City_Id, distance(City.City_description_embedding, ref_vec_0) AS distance\n FROM City\n ORDER BY distance\n LIMIT 1\n),\n\nVenueMatch AS (\n SELECT Venue_Id\n FROM Venue v\n JOIN CityMatch cm ON toString(v.City_Id) = toString(cm.City_Id)\n)\n\nSELECT p.Player_Name\nFROM Match m\nJOIN VenueMatch vm ON toString(m.Venue_Id) = toString(vm.Venue_Id)\nJOIN Player p ON toString(m.Man_of_the_Match) = toString(p.Player_Id)\nWHERE m.Match_Id IN (\n SELECT Match_Id\n FROM Match\n WHERE Venue_Id = vm.Venue_Id\n)\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'vm.Venue_Id' while processing query: 'WITH [0.0965883657336235, 0.059014853090047836, -0.0005645405617542565, -0.00647771218791604, -0.016407493501901627, 0.03167818486690521, -0.02909514680504799, -0.025497568771243095, -0.12790383398532867, 0.09439785778522491, -0.004724831786006689, -0.12379512935876846, -0.02420962229371071, 0.037725675851106644, 0.00000771549457567744, 0.026305316016077995, 0.05868907645344734, -0.07744811475276947, 0.03486206755042076, -0.0919523686170578, -0.08885885030031204, 0.019943993538618088, 0.04002295807003975, -0.014428656548261642, 0.04428888484835625, 0.07481686025857925, 0.024088546633720398, 0.11222876608371735, -0.030138414353132248, -0.008376832120120525, 0.02178950048983097, 0.066807322204113, 0.029792005196213722, 0.014251361601054668, -0.0023989647161215544, -0.005424465052783489, -0.005662416573613882, 0.04957946389913559, 0.081123948097229, -0.02125963754951954, 0.06347861140966415, -0.020699327811598778, 0.00543561577796936, 0.05141782760620117, -0.07280020415782928, 0.006803314667195082, 0.01783066987991333, 0.09773309528827667, 0.04317731410264969, -0.048077356070280075, 0.05068320035934448, 0.016798298805952072, 0.04810485988855362, -0.06200364604592323, 0.07323618233203888, -0.01329092774540186, -0.03459153696894646, 0.043004054576158524, 0.026916563510894775, -0.005653283093124628, 0.026678940281271935, 0.017909547314047813, -0.04580822214484215, 0.04784518480300903, 0.0332125686109066, -0.07135236263275146, -0.037529703229665756, 0.06045292317867279, 0.031161969527602196, 0.004581274464726448, 0.061671484261751175, -0.05087855085730553, -0.03766374662518501, -0.03729001432657242, -0.006415840238332748, 0.018574081361293793, -0.012657895684242249, -0.012225762009620667, -0.041833437979221344, -0.0012306421995162964, 0.035081956535577774, -0.07461201399564743, -0.029412008821964264, 0.031465426087379456, -0.05335850268602371, -0.0009093411499634385, 0.050194211304187775, -0.028714830055832863, -0.01708865538239479, -0.04122448340058327, -0.014616874046623707, -0.009922034107148647, -0.023846717551350594, -0.023218462243676186, -0.0032919275108724833, 0.05080925300717354, -0.07082945108413696, -0.049215421080589294, 0.013787362724542618, 0.09365120530128479, -0.061086345463991165, -0.02336166426539421, 0.0806322991847992, 0.027993468567728996, -0.02176492288708687, -0.07916778326034546, -0.05726589262485504, 0.039451975375413895, -0.027234071865677834, 0.0005198197322897613, 0.004873205441981554, 0.030530979856848717, -0.0668170303106308, 0.07588057219982147, -0.011606893502175808, -0.018558332696557045, -0.0434567853808403, 0.02654465101659298, -0.012265664525330067, -0.009170936420559883, 0.049398329108953476, -0.02770289219915867, -0.13164828717708588, -0.011229868978261948, -0.009480670094490051, -0.03871173784136772, 0.027268223464488983, -3.169443173793901e-33, -0.0091064078733325, -0.03809048607945442, 0.004760706331580877, 0.04657101258635521, 0.008465517312288284, -0.09016004204750061, -0.011289108544588089, -0.014097137376666069, 0.007573928218334913, -0.008080870844423771, 0.08272532373666763, -0.020270410925149918, 0.019289929419755936, -0.028217939659953117, 0.08621842414140701, -0.04399314150214195, -0.03408785164356232, 0.012767251580953598, -0.0025665927678346634, -0.01742854341864586, -0.0338214635848999, -0.004829640034586191, 0.04591500759124756, -0.12585094571113586, -0.0772351399064064, -0.007889782078564167, 0.033568672835826874, -0.08046227693557739, 0.1157781258225441, 0.013725748285651207, 0.039303943514823914, 0.026303915306925774, 0.014999249950051308, 0.041612088680267334, 0.0037565636448562145, 0.03795363008975983, -0.01128571480512619, -0.12192662060260773, 0.04512472450733185, 0.003451390890404582, -0.06281223893165588, -0.04613441973924637, -0.10326210409402847, 0.018287459388375282, -0.0502111054956913, 0.06365534663200378, -0.06706781685352325, -0.049234017729759216, -0.016981976106762886, 0.0023912694305181503, 0.10291433334350586, -0.03025713935494423, -0.04380418732762337, 0.0017059158999472857, -0.004689186345785856, 0.00922996923327446, -0.0009676633053459227, -0.033005375415086746, 0.028415624052286148, 0.011758826673030853, 0.014375235885381699, -0.017869621515274048, -0.049616627395153046, 0.045104995369911194, 0.054910968989133835, -0.00606094254180789, 0.05036350339651108, -0.02977011725306511, 0.034152183681726456, 0.0065976036712527275, 0.06613560020923615, 0.014453684911131859, 0.021078871563076973, 0.014817588962614536, 0.002845033071935177, 0.08259855955839157, 0.006797317415475845, 0.07476222515106201, -0.012522300705313683, 0.021658586338162422, -0.029421735554933548, -0.013340631499886513, -0.07384062558412552, -0.03600693494081497, 0.12858591973781586, 0.02257012203335762, -0.008921000175178051, -0.0958063080906868, 0.035952769219875336, 0.02713555470108986, -0.080459363758564, -0.0333549790084362, -0.04167564958333969, -0.021359043195843697, -0.034943971782922745, 2.47404069006785e-34, -0.024332523345947266, 0.02083462104201317, -0.030133359134197235, -0.010417643003165722, -0.0276253130286932, 0.004447919316589832, -0.044301483780145645, 0.0601077526807785, -0.028777966275811195, -0.004041215404868126, -0.08184929937124252, 0.03512442484498024, 0.06894112378358841, 0.020917436107993126, 0.023456204682588577, -0.005060496740043163, 0.13285624980926514, 0.005913588684052229, -0.0768776535987854, 0.03792759031057358, 0.027991633862257004, -0.053295671939849854, -0.04862671345472336, -0.040292274206876755, -0.036167703568935394, 0.05497616529464722, -0.13653342425823212, -0.04978631064295769, -0.045111458748579025, 0.030802154913544655, -0.04954448714852333, 0.046889711171388626, -0.007096868474036455, 0.0028789557982236147, -0.05978168547153473, 0.09149104356765747, 0.07993567734956741, -0.031160321086645126, 0.02031996287405491, 0.04872352257370949, -0.03467418625950813, 0.034727271646261215, -0.026037629693746567, 0.04537403956055641, 0.0718756839632988, -0.031823739409446716, -0.10301030427217484, 0.050357718020677567, -0.010444925166666508, 0.006842718925327063, 0.050595175474882126, 0.12964914739131927, -0.011912401765584946, 0.01605825126171112, 0.023484934121370316, 0.054290350526571274, 0.019131679087877274, -0.03683902323246002, -0.051667697727680206, -0.15352536737918854, -0.08284946531057358, 0.011758360080420971, -0.08178173005580902, 0.10353019088506699, -0.02029791660606861, 0.05596054717898369, 0.012446918524801731, -0.1349545568227768, -0.023292280733585358, -0.06543762981891632, -0.07446392625570297, -0.011751905083656311, -0.07550258189439774, 0.0034808283671736717, -0.0492582730948925, 0.07146070897579193, 0.04451914131641388, 0.09646860510110855, 0.04038471356034279, 0.1345813125371933, -0.001127849449403584, 0.11563671380281448, -0.012509630061686039, -0.042124830186367035, 0.04025387763977051, 0.06610056757926941, -0.038427531719207764, -0.08150312304496765, 0.025063561275601387, 0.014683045446872711, 0.008434739895164967, 0.04630816727876663, -0.056897539645433426, -0.07264263927936554, 0.03201955556869507, -1.6514448475390964e-8, -0.0335996076464653, 0.022925199940800667, -0.0677303895354271, 0.03009333834052086, 0.08290979266166687, -0.024831918999552727, 0.011170038022100925, 0.04646804556250572, 0.08286289125680923, 0.003279011929407716, -0.029278866946697235, -0.0011566471075639129, -0.008107631467282772, 0.018712613731622696, -0.01965964399278164, 0.01587149314582348, 0.012976661324501038, -0.009412411600351334, -0.07421008497476578, 0.005248174536973238, -0.017518766224384308, 0.03813984990119934, 0.026193959638476372, 0.017944402992725372, 0.006194045767188072, -0.001563455443829298, -0.051806844770908356, -0.07437270134687424, -0.008022528141736984, 0.026283565908670425, 0.04624207317829132, 0.09543826431035995, -0.03673657774925232, -0.055934514850378036, 0.02361290156841278, -0.022794751450419426, 0.07252761721611023, -0.04271678254008293, 0.024938225746154785, -0.050556059926748276, -0.04551202803850174, 0.04427333176136017, -0.003149323631078005, 0.07272286713123322, 0.07553460448980331, -0.02857125550508499, 0.0006470636581070721, 0.017446955665946007, -0.04092477262020111, -0.1415545493364334, -0.016937414184212685, 0.06304251402616501, 0.02930520288646221, -0.026156214997172356, -0.05375507473945618, -0.03336212411522865, -0.10592032968997955, 0.008254890330135822, -0.07804981619119644, -0.010987071320414543, 0.05171379819512367, -0.0652269572019577, -0.0028532969299703836, 0.06356347352266312] AS ref_vec_0 SELECT Match_Id FROM Match WHERE Venue_Id = vm.Venue_Id', required columns: 'Match_Id' 'Venue_Id' 'vm.Venue_Id', maybe you meant: 'Match_Id' or 'Venue_Id': While processing Match_Id IN ((WITH [0.0965883657336235, 0.059014853090047836, -0.0005645405617542565, -0.00647771218791604, -0.016407493501901627, 0.03167818486690521, -0.02909514680504799, -0.025497568771243095, -0.12790383398532867, 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0.08290979266166687, -0.024831918999552727, 0.011170038022100925, 0.04646804556250572, 0.08286289125680923, 0.003279011929407716, -0.029278866946697235, -0.0011566471075639129, -0.008107631467282772, 0.018712613731622696, -0.01965964399278164, 0.01587149314582348, 0.012976661324501038, -0.009412411600351334, -0.07421008497476578, 0.005248174536973238, -0.017518766224384308, 0.03813984990119934, 0.026193959638476372, 0.017944402992725372, 0.006194045767188072, -0.001563455443829298, -0.051806844770908356, -0.07437270134687424, -0.008022528141736984, 0.026283565908670425, 0.04624207317829132, 0.09543826431035995, -0.03673657774925232, -0.055934514850378036, 0.02361290156841278, -0.022794751450419426, 0.07252761721611023, -0.04271678254008293, 0.024938225746154785, -0.050556059926748276, -0.04551202803850174, 0.04427333176136017, -0.003149323631078005, 0.07272286713123322, 0.07553460448980331, -0.02857125550508499, 0.0006470636581070721, 0.017446955665946007, -0.04092477262020111, -0.1415545493364334, -0.016937414184212685, 0.06304251402616501, 0.02930520288646221, -0.026156214997172356, -0.05375507473945618, -0.03336212411522865, -0.10592032968997955, 0.008254890330135822, -0.07804981619119644, -0.010987071320414543, 0.05171379819512367, -0.0652269572019577, -0.0028532969299703836, 0.06356347352266312] AS ref_vec_0 SELECT Match_Id FROM Match WHERE Venue_Id = vm.Venue_Id) AS _subquery9). (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A right-handed batsman from England known for his aggressive style.') AS ref_vec_0,\n\nPlayerVectorSearch AS (\n SELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT Player_Name\nFROM PlayerVectorSearch;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "What is the name of the player who is a top match for being described as a right-handed batsman from England known for his aggressive style?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A right-handed batsman from England known for his aggressive style.') AS ref_vec_0,\n\nPlayerVectorSearch AS (\n SELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT Player_Name\nFROM PlayerVectorSearch;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its sports events') AS ref_vec_0\n\nSELECT v.Venue_Name, distance(c.City_description_embedding, ref_vec_0) AS distance\nFROM Venue v\nJOIN City c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "**\n\nPlease find the names of venues located in the top 3 cities that are vibrant and known for their sports events.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its sports events') AS ref_vec_0\n\nSELECT v.Venue_Name, distance(c.City_description_embedding, ref_vec_0) AS distance\nFROM Venue v\nJOIN City c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A skilled batsman with a consistent performance record and recognized for an outstanding play.') AS ref_vec_0\n\nSELECT m.Match_Date, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Match m ON toString(p.Player_Id) = toString(m.Man_of_the_Match)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you tell me the dates of matches where the top 3 players, known for being skilled batsmen with consistent performance and recognized for outstanding play, were named Man of the Match?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A skilled batsman with a consistent performance record and recognized for an outstanding play.') AS ref_vec_0\n\nSELECT m.Match_Date, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Match m ON toString(p.Player_Id) = toString(m.Man_of_the_Match)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A top-performing player with exceptional skills in the 2023 season') AS ref_vec_0\n\nSELECT p.Player_Name, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Player_Match pm ON toString(p.Player_Id) = toString(pm.Player_Id)\nJOIN Match m ON toString(pm.Match_Id) = toString(m.Match_Id)\nWHERE m.Season_Id = (SELECT Season_Id FROM Season WHERE Season_Year = 2023)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the top player with exceptional skills in the 2023 season? I need to know their name.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-performing player with exceptional skills in the 2023 season') AS ref_vec_0\n\nSELECT p.Player_Name, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Player_Match pm ON toString(p.Player_Id) = toString(pm.Player_Id)\nJOIN Match m ON toString(pm.Match_Id) = toString(m.Match_Id)\nWHERE m.Season_Id = (SELECT Season_Id FROM Season WHERE Season_Year = 2023)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'Player_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium with modern facilities and large audience capacity') AS ref_vec_0\n\nSELECT v.Venue_Name, distance(v.Venue_description_embedding, ref_vec_0) AS distance\nFROM Venue v\nJOIN City c ON toString(v.City_Id) = toString(c.City_Id)\nJOIN Country co ON toString(c.Country_id) = toString(co.Country_Id)\nWHERE co.Country_Name = 'India'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 4, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I am interested in finding the top 5 venues in India that are described as stadiums with modern facilities and large audience capacity. Could you provide me these venue names, sorted by how closely they match the description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium with modern facilities and large audience capacity') AS ref_vec_0\n\nSELECT v.Venue_Name, distance(v.Venue_description_embedding, ref_vec_0) AS distance\nFROM Venue v\nJOIN City c ON toString(v.City_Id) = toString(c.City_Id)\nJOIN Country co ON toString(c.Country_id) = toString(co.Country_Id)\nWHERE co.Country_Name = 'India'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'Venue_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned stadium known for hosting international matches') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A vibrant city with historical significance in sports') AS ref_vec_1,\n\nv_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 3\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(City_description_embedding, ref_vec_1) AS distance\n FROM City\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT v.Venue_Name\nFROM v_filtered AS v\nJOIN c_filtered AS c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY v.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the venue that’s like a top-notch stadium famous for international games and is in a lively city with a rich sports history? I just need the name of that venue.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned stadium known for hosting international matches') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A vibrant city with historical significance in sports') AS ref_vec_1,\n\nv_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 3\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(City_description_embedding, ref_vec_1) AS distance\n FROM City\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT v.Venue_Name\nFROM v_filtered AS v\nJOIN c_filtered AS c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY v.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium: 123 Main St, New York, NY. Capacity: 50,000. Home team: Guardians') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cricketer known for remarkable batting skills and sharp fielding') AS ref_vec_1,\n\nVenue_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 5\n),\n\nPlayer_filtered AS (\n SELECT\n *,\n distance(Player_description_embedding, ref_vec_1) AS distance\n FROM Player\n WHERE Player_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Cricketer known for remarkable batting skills AND sharp fielding')\n ORDER BY distance\n LIMIT 3\n),\n\nMatchDetails AS (\n SELECT \n Match.Match_Id AS Match_Id,\n Match.Match_Date AS Match_Date,\n Venue.Venue_Name AS Venue_Name\n FROM \n Match\n JOIN Venue \n ON toString(Match.Venue_Id) = toString(Venue.Venue_Id)\n)\n\nSELECT \n Player.Player_Name AS Player_Name, \n MatchDetails.Venue_Name AS Venue_Name\nFROM Player_filtered AS Player Player_Match \n ON toString(Player.Player_Id) = toString(Player_Match.Player_Id)\nJOIN MatchDetails \n ON toString(Player_Match.Match_Id) = toString(MatchDetails.Match_Id);", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I want to find the names of the top 3 players known for their exceptional batting skills and sharp fielding, along with the names of the top 5 venues where matches were held, specifically matching a stadium located at 123 Main St, New York, NY with a capacity of 50,000 and home to the Guardians.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium: 123 Main St, New York, NY. Capacity: 50,000. Home team: Guardians') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cricketer known for remarkable batting skills and sharp fielding') AS ref_vec_1,\n\nVenue_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 5\n),\n\nPlayer_filtered AS (\n SELECT\n *,\n distance(Player_description_embedding, ref_vec_1) AS distance\n FROM Player\n WHERE Player_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Cricketer known for remarkable batting skills AND sharp fielding')\n ORDER BY distance\n LIMIT 3\n),\n\nMatchDetails AS (\n SELECT \n Match.Match_Id AS Match_Id,\n Match.Match_Date AS Match_Date,\n Venue.Venue_Name AS Venue_Name\n FROM \n Match\n JOIN Venue \n ON toString(Match.Venue_Id) = toString(Venue.Venue_Id)\n)\n\nSELECT \n Player.Player_Name AS Player_Name, \n MatchDetails.Venue_Name AS Venue_Name\nFROM Player_filtered AS Player Player_Match \n ON toString(Player.Player_Id) = toString(Player_Match.Player_Id)\nJOIN MatchDetails \n ON toString(Player_Match.Match_Id) = toString(MatchDetails.Match_Id);" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17222 ('MATCH') (line 20, col 40): MATCH [0.03060474433004856, 0.05281533673405647, -0.05939742550253868, -0.005899240728467703, -0.05323641002178192, 0.009977047331631184, 0.09690775722265244, 0. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its historical sights and cultural heritage') AS ref_vec_0\n\nSELECT City_Id, City_Name, distance(City.City_description_embedding, ref_vec_0) AS distance \nFROM City\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which city is most recognized for its vibrant historical sights and cultural heritage?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its historical sights and cultural heritage') AS ref_vec_0\n\nSELECT City_Id, City_Name, distance(City.City_description_embedding, ref_vec_0) AS distance \nFROM City\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional cricket player with outstanding skills') AS ref_vec_0\n\nSELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Who are the top 3 cricket players known for their exceptional skills?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional cricket player with outstanding skills') AS ref_vec_0\n\nSELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A talented player known for exceptional performance under pressure') AS ref_vec_0,\n\nSimilarPlayers AS (\n SELECT Player_Id, Player_Name, Country_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n sp.Player_Name AS Player_Name,\n t.Team_Name AS Team_Name,\n m.Match_Date AS Match_Date,\n m.Win_Margin AS Win_Margin\nFROM\n SimilarPlayers sp\nJOIN\n Player_Match pm ON toString(sp.Player_Id) = toString(pm.Player_Id)\nJOIN\n Match m ON toString(pm.Match_Id) = toString(m.Match_Id)\nJOIN\n Team t ON toString(m.Match_Winner) = toString(t.Team_Id)\nWHERE\n m.Win_Margin > 50\nORDER BY\n m.Win_Margin DESC\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I am interested in finding the top 5 matches where players, who are known for their exceptional performance under pressure, played and won with a margin greater than 50. Please provide the names of these players, their team names, the dates of the matches, and the win margins, sorted by the win margins in descending order.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A talented player known for exceptional performance under pressure') AS ref_vec_0,\n\nSimilarPlayers AS (\n SELECT Player_Id, Player_Name, Country_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n sp.Player_Name AS Player_Name,\n t.Team_Name AS Team_Name,\n m.Match_Date AS Match_Date,\n m.Win_Margin AS Win_Margin\nFROM\n SimilarPlayers sp\nJOIN\n Player_Match pm ON toString(sp.Player_Id) = toString(pm.Player_Id)\nJOIN\n Match m ON toString(pm.Match_Id) = toString(m.Match_Id)\nJOIN\n Team t ON toString(m.Match_Winner) = toString(t.Team_Id)\nWHERE\n m.Win_Margin > 50\nORDER BY\n m.Win_Margin DESC\nLIMIT 5;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis with a rich history and vibrant culture') AS ref_vec_0\n\nSELECT City_Id, City_Name, Country_id, City_description, distance(City.City_description_embedding, ref_vec_0) AS distance\nFROM City\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 5, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the vast tapestry of cities, which one stands as the vibrant epicenter of history and culture, echoing the lively spirit of a bustling metropolis? Reveal its identity, name, belonging country, and the measure of its proximity to this spirited essence.", + "external_knowledge": "- The `MATCH` operator in the query is designed to find vector embeddings that are most similar to a specified text concept, in this case, using the \"all-MiniLM-L6-v2\" model.\n- The vector search performed is an approximate nearest neighbor (ANN) search, which efficiently identifies items that are closest in vector space.\n- Euclidean distance (L2 norm) is typically used to determine similarity, where a smaller distance indicates higher similarity.\n- The concept of \"A bustling metropolis with a rich history and vibrant culture\" serves as the semantic benchmark for comparison.\n- The `LIMIT 1` clause ensures that only the city most closely matching this concept is returned.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis with a rich history and vibrant culture') AS ref_vec_0\n\nSELECT City_Id, City_Name, Country_id, City_description, distance(City.City_description_embedding, ref_vec_0) AS distance\nFROM City\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A cricket player from Australia known for left-handed batting') AS ref_vec_0\n\nSELECT p.Player_Name, p.Batting_hand, distance(p.Player_description_embedding, ref_vec_0) AS distance \nFROM Player p\nJOIN Batting_Style bs ON toString(p.Batting_hand) = toString(bs.Batting_Id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the top 3 cricket players from the database who are most like an Australian player known for left-handed batting, including their names, batting styles, and how closely they match this description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A cricket player from Australia known for left-handed batting') AS ref_vec_0\n\nSELECT p.Player_Name, p.Batting_hand, distance(p.Player_description_embedding, ref_vec_0) AS distance \nFROM Player p\nJOIN Batting_Style bs ON toString(p.Batting_hand) = toString(bs.Batting_Id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium located in a metropolitan area with high capacity') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Season featuring intense competition and remarkable players') AS ref_vec_1,\n\nv_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(Season_description_embedding, ref_vec_1) AS distance\n FROM Season\n WHERE Season_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Season featuring intense competition AND remarkable players')\n ORDER BY distance\n LIMIT 3\n),\n\nVenueMatches AS (\n SELECT\n m.Match_Id AS Match_Id,\n v.Venue_Name AS Venue_Name,\n c.City_Name AS City_Name\n FROM\n Match m\n JOIN v_filtered AS v ON toString(m.Venue_Id) = toString(v.Venue_Id)\n JOIN City c ON toString(v.City_Id) = toString(c.City_Id)\n),\n\nSeasonDetails AS (\n SELECT\n s.Season_Id AS Season_Id,\n s.Season_Year AS Season_Year,\n s.Season_description AS Season_description,\n distance\n FROM s_filtered AS s\n)\n\nSELECT\n vm.Match_Id AS Match_Id,\n vm.Venue_Name AS Venue_Name,\n sd.Season_Year AS Season_Year\nFROM\n VenueMatches vm\nJOIN SeasonDetails sd ON toString(vm.Match_Id) = toString(sd.Season_Id)\nORDER BY\n sd.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Can you provide me with the match IDs, venue names, and the years of the top 10 matches held in venues recognized as large-capacity stadiums in metropolitan areas and within seasons noted for intense competition and exceptional players?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium located in a metropolitan area with high capacity') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Season featuring intense competition and remarkable players') AS ref_vec_1,\n\nv_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(Season_description_embedding, ref_vec_1) AS distance\n FROM Season\n WHERE Season_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Season featuring intense competition AND remarkable players')\n ORDER BY distance\n LIMIT 3\n),\n\nVenueMatches AS (\n SELECT\n m.Match_Id AS Match_Id,\n v.Venue_Name AS Venue_Name,\n c.City_Name AS City_Name\n FROM\n Match m\n JOIN v_filtered AS v ON toString(m.Venue_Id) = toString(v.Venue_Id)\n JOIN City c ON toString(v.City_Id) = toString(c.City_Id)\n),\n\nSeasonDetails AS (\n SELECT\n s.Season_Id AS Season_Id,\n s.Season_Year AS Season_Year,\n s.Season_description AS Season_description,\n distance\n FROM s_filtered AS s\n)\n\nSELECT\n vm.Match_Id AS Match_Id,\n vm.Venue_Name AS Venue_Name,\n sd.Season_Year AS Season_Year\nFROM\n VenueMatches vm\nJOIN SeasonDetails sd ON toString(vm.Match_Id) = toString(sd.Season_Id)\nORDER BY\n sd.distance AS distance\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17224 ('MATCH') (line 20, col 40): MATCH [-0.0008379089995287359, 0.01756220869719982, -0.024012723937630653, -0.038436681032180786, 0.04193168878555298, 0.11420555412769318, 0.012817234732210636. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An outstanding cricketer known for precision and agility') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A leading cricket nation with a rich history in the sport') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n WHERE Player_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'An outstanding cricketer known for precision\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(Country_description_embedding, ref_vec_1) AS distance\n FROM Country\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.Player_Name AS Player_Name, \n c.Country_Name AS Country_Name, \n PERCENT_RANK() OVER ( WHERE agility') ORDER BY p.distance) AS Rank\nFROM p_filtered AS p\nJOIN c_filtered AS c ON toString(p.Country_Name) = toString(c.Country_Id)\nORDER BY \n p.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 4, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Please identify the top 10 players known for precision and agility in cricket, and list their names along with the countries recognized for having a rich history in cricket. Provide their rank based on proximity to these descriptions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An outstanding cricketer known for precision and agility') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A leading cricket nation with a rich history in the sport') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n WHERE Player_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'An outstanding cricketer known for precision\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(Country_description_embedding, ref_vec_1) AS distance\n FROM Country\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.Player_Name AS Player_Name, \n c.Country_Name AS Country_Name, \n PERCENT_RANK() OVER ( WHERE agility') ORDER BY p.distance) AS Rank\nFROM p_filtered AS p\nJOIN c_filtered AS c ON toString(p.Country_Name) = toString(c.Country_Id)\nORDER BY \n p.distance AS distance\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17065 ('MATCH') (line 10, col 40): MATCH lembed('all-MiniLM-L6-v2', 'An outstanding cricketer known for precision\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n . Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned cricketer from Australia known for his aggressive batting style') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the cricketer from Australia who's famous for his aggressive batting style? I'm looking for just one name that really fits that description.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned cricketer from Australia known for his aggressive batting style') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned Indian cricketer known for exceptional skill') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I would like to know the name of the player who is most recognized as an exceptional Indian cricketer.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned Indian cricketer known for exceptional skill') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An excellent cricket player with outstanding batting skills') AS ref_vec_0\n\nSELECT p.Player_Name, c.Country_Name, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Country c ON toString(p.Country_Name) = toString(c.Country_Id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you tell me the names and countries of the top five cricket players who are known for being truly outstanding with their batting skills?", + "external_knowledge": "Vector searches using the 'MATCH' operator perform an approximate nearest neighbor (ANN) search, which compares vector embeddings based on semantic similarity. In this query, the 'lembed' function is employed with the 'all-MiniLM-L6-v2' model to find players whose descriptions are semantically similar to \"An excellent cricket player with outstanding batting skills\". The parameter 'k=5' specifies that the search is limited to the top 5 players, with similarity determined by proximity in the vector space. These searches help identify entities that are conceptually close to a specified description, leveraging the power of natural language processing models to interpret and rank the results.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An excellent cricket player with outstanding batting skills') AS ref_vec_0\n\nSELECT p.Player_Name, c.Country_Name, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Country c ON toString(p.Country_Name) = toString(c.Country_Id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2'', 'A brewery known for its distinctive craft beers in a bustling city environment.') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "**\n\nCould you please find the name of the beer that belongs to the top brewery known for its distinctive craft beers in a bustling city environment?\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2'', 'A top brewery famous for its unique craft beers located in a vibrant urban area.') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance FROM beers b JOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2'', 'Renowned brewery producing distinct craft beers in a lively city setting.') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance FROM beers b JOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2'', 'Leading brewery known for exceptional craft beers in a bustling metropolitan environment.') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance FROM beers b JOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2'', 'Famous brewery with standout craft beers in an energetic urban locale.') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance FROM beers b JOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2'', 'Premier brewery recognized for its craft beer excellence in a dynamic city atmosphere.') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance FROM beers b JOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 118 ('') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1\n FORMAT Native') (line 2, col 113): ') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN breweries br ON toString(b.brewery_id) = t. Single quoted string is not closed: '') AS ref_vec_0\n\nSELECT b.name, distance(br.breweries_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 1\n FORMAT Native'. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'versatile cricketer with impressive batting') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'cricketing nation with rich history') AS ref_vec_1,\n\nPlayer_filtered AS (\n SELECT\n *,\n distance(Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n\n ORDER BY distance\n LIMIT 5\n),\n\nCountry_filtered AS (\n SELECT\n *,\n distance(Country_description_embedding, ref_vec_1) AS distance\n FROM Country\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarPlayers AS (\n SELECT Player_Id, Player_Name, distance\n FROM Player_filtered AS Player BY distance\n),\n\nPlayerRoles AS (\n SELECT pm.Player_Id, r.Role_Desc\n FROM Player_Match pm\n JOIN Rolee r ON toString(pm.Role_Id) = toString(r.Role_Id)\n),\n\nCountryInfo AS (\n SELECT Country_Id, Country_Name\n FROM Country_filtered AS Country BY distance\n)\n\nSELECT p.Player_Name, pr.Role_Desc, c.Country_Name\nFROM SimilarPlayers sp\nJOIN Player p ON toString(sp.Player_Id) = toString(p.Player_Id)\nJOIN PlayerRoles pr ON toString(p.Player_Id) = toString(pr.Player_Id)\nJOIN CountryInfo c ON toString(p.Country_Name) = toString(c.Country_Id)\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 10, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Can you uncover the jewels of the cricketing world by finding the top 5 players known for their versatile and impressive batting skills, along with their roles and the countries celebrated for rich cricketing traditions?", + "external_knowledge": "- The `MATCH` operator in the query is used to perform an approximate nearest neighbor (ANN) search. This helps in finding entities that are similar to a given textual description or concept.\n- The parameter `k=5` in the SimilarPlayers CTE indicates that the search is limited to the top 5 players with descriptions matching the vector for \"versatile cricketer with impressive batting.\"\n- Similarly, `k=3` in the CountryInfo CTE ensures the retrieval of the top 3 countries with descriptions matching \"cricketing nation with rich history.\"\n- The embeddings are compared using Euclidean distance, where a smaller distance implies higher similarity.\n- The metaphorical expressions \"versatile cricketer with impressive batting\" and \"cricketing nation with rich history\" guide the vector search to find players and countries fitting these descriptions.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'versatile cricketer with impressive batting') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'cricketing nation with rich history') AS ref_vec_1,\n\nPlayer_filtered AS (\n SELECT\n *,\n distance(Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n\n ORDER BY distance\n LIMIT 5\n),\n\nCountry_filtered AS (\n SELECT\n *,\n distance(Country_description_embedding, ref_vec_1) AS distance\n FROM Country\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarPlayers AS (\n SELECT Player_Id, Player_Name, distance\n FROM Player_filtered AS Player BY distance\n),\n\nPlayerRoles AS (\n SELECT pm.Player_Id, r.Role_Desc\n FROM Player_Match pm\n JOIN Rolee r ON toString(pm.Role_Id) = toString(r.Role_Id)\n),\n\nCountryInfo AS (\n SELECT Country_Id, Country_Name\n FROM Country_filtered AS Country BY distance\n)\n\nSELECT p.Player_Name, pr.Role_Desc, c.Country_Name\nFROM SimilarPlayers sp\nJOIN Player p ON toString(sp.Player_Id) = toString(p.Player_Id)\nJOIN PlayerRoles pr ON toString(p.Player_Id) = toString(pr.Player_Id)\nJOIN CountryInfo c ON toString(p.Country_Name) = toString(c.Country_Id)\nLIMIT 10;" + ], + "integration_level": 7, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17345 ('BY') (line 27, col 40): BY distance\n),\n\nPlayerRoles AS (\n SELECT pm.Player_Id, r.Role_Desc\n FROM Player_Match pm\n JOIN Rolee r ON toString(pm.Role_Id) = toString(r.Rol. Expected one of: FINAL, SAMPLE, table, table function, subquery or list of joined tables, array join, LEFT ARRAY JOIN, INNER, ARRAY JOIN, GLOBAL, LOCAL, ANY, ALL, ASOF, SEMI, ANTI, ONLY, LEFT, RIGHT, FULL, CROSS, PASTE, JOIN, PREWHERE, WHERE, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'What is your favorite book and why?') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'survey about reading habits and preferences') AS ref_vec_1,\n\nq_filtered AS (\n SELECT\n *,\n distance(questiontext_embedding, ref_vec_0) AS distance\n FROM Question\n WHERE questiontext_embedding MATCH lembed('all-MiniLM-L6-v2', 'What is your favorite book AND why?')\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(Description_embedding, ref_vec_1) AS distance\n FROM Survey\n WHERE Description_embedding MATCH lembed('all-MiniLM-L6-v2', 'survey about reading habits AND preferences')\n ORDER BY distance\n LIMIT 5\n),\n\nQuestionVectorSearch AS (\n SELECT q.questionid, q.questiontext, q.distance\n FROM q_filtered AS q\n),\n\nSurveyVectorSearch AS (\n SELECT s.SurveyID, s.Description, s.distance\n FROM s_filtered AS s\n)\n\nSELECT a.UserID, a.AnswerText\nFROM Answer a\nJOIN QuestionVectorSearch qvs ON toString(a.QuestionID) = toString(qvs.questionid)\nJOIN SurveyVectorSearch svs ON toString(a.SurveyID) = toString(svs.SurveyID)\nORDER BY qvs.distance, svs.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Please provide the user IDs and text of the 10 best answers to survey questions that ask about favorite books and are part of surveys concerning reading habits and preferences.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'What is your favorite book and why?') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'survey about reading habits and preferences') AS ref_vec_1,\n\nq_filtered AS (\n SELECT\n *,\n distance(questiontext_embedding, ref_vec_0) AS distance\n FROM Question\n WHERE questiontext_embedding MATCH lembed('all-MiniLM-L6-v2', 'What is your favorite book AND why?')\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(Description_embedding, ref_vec_1) AS distance\n FROM Survey\n WHERE Description_embedding MATCH lembed('all-MiniLM-L6-v2', 'survey about reading habits AND preferences')\n ORDER BY distance\n LIMIT 5\n),\n\nQuestionVectorSearch AS (\n SELECT q.questionid, q.questiontext, q.distance\n FROM q_filtered AS q\n),\n\nSurveyVectorSearch AS (\n SELECT s.SurveyID, s.Description, s.distance\n FROM s_filtered AS s\n)\n\nSELECT a.UserID, a.AnswerText\nFROM Answer a\nJOIN QuestionVectorSearch qvs ON toString(a.QuestionID) = toString(qvs.questionid)\nJOIN SurveyVectorSearch svs ON toString(a.SurveyID) = toString(svs.SurveyID)\nORDER BY qvs.distance, svs.distance\nLIMIT 10;" + ], + "integration_level": 7, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17090 ('MATCH') (line 10, col 34): MATCH [-0.0845746397972107, -0.012573502026498318, -0.011397247202694416, 0.038920849561691284, -0.029807427898049355, 0.01608928106725216, 0.003246395383030176. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0\n\nSELECT SurveyID, Description, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "What are three surveys that seem to deal with the 2020 mental health topic?", + "external_knowledge": "- The `MATCH` operator performs approximate nearest neighbor (ANN) search, allowing retrieval of items similar to a given vector.\n- The `k = 3` parameter limits the results to the top three most similar items according to the vector similarity search.\n- Vector embeddings are produced using the \"all-MiniLM-L6-v2\" model, which is a transformer model designed for handling semantic similarity and sentence embeddings.\n- The similarity between vectors typically uses Euclidean distance (L2 norm) by default; surveys with smaller distances are considered more semantically similar to the search phrase.\n- Understanding the domain context: \"mental health survey for 2020\" implies surveys related to mental health conducted in the year 2020.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0\n\nSELECT SurveyID, Description, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0,\n\nSimilarSurveys AS (\n SELECT \n SurveyID, \n Description, \n distance(Survey.Description_embedding, ref_vec_0) AS distance\n FROM \n Survey\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n Description\nFROM \n SimilarSurveys;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Can you provide the description of the survey that best matches the topic of \"mental health survey for 2020\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0,\n\nSimilarSurveys AS (\n SELECT \n SurveyID, \n Description, \n distance(Survey.Description_embedding, ref_vec_0) AS distance\n FROM \n Survey\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n Description\nFROM \n SimilarSurveys;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey report for the recent year') AS ref_vec_0\n\nSELECT SurveyID, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the top 3 surveys related to a recent mental health survey report? Please provide their IDs and similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey report for the recent year') AS ref_vec_0\n\nSELECT SurveyID, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'What is your profession?') AS ref_vec_0\n\nSELECT questionid, distance(Question.questiontext_embedding, ref_vec_0) AS distance\nFROM Question\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID of the question most related to \"What is your profession?\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'What is your profession?') AS ref_vec_0\n\nSELECT questionid, distance(Question.questiontext_embedding, ref_vec_0) AS distance\nFROM Question\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Knife or blade weapon') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the case numbers and locations for the top 5 incidents involving knife or blade weapons?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Knife or blade weapon') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Knife') AS ref_vec_0\n\nSELECT case_number, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the three incidents where the weapon used is most representative of a knife, and provide me with their case numbers?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Knife') AS ref_vec_0\n\nSELECT case_number, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Critical Condition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Assault Rifle') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_statuses_embedding, ref_vec_0) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_weapon_embedding, ref_vec_1) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_status_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_statuses AS subject_statuses, \n i.subject_weapon AS subject_weapon,\n s.full_name AS subject_name,\n distance\n FROM i_filtered AS i\n JOIN subjects s ON toString(i.case_number) = toString(s.case_number)\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_weapon_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_weapon AS subject_weapon, \n i.subject_statuses AS subject_statuses,\n o.full_name AS officer_name,\n distance\n FROM i_filtered AS i\n JOIN officers o ON toString(i.case_number) = toString(o.case_number)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ssa.case_number AS case_number, \n ssa.date AS date, \n ssa.subject_name AS subject_name, \n swa.officer_name AS officer_name, \n ssa.subject_statuses AS subject_statuses, \n swa.subject_weapon AS subject_weapon\nFROM subject_status_analysis ssa\nJOIN subject_weapon_analysis swa ON toString(ssa.case_number) = toString(swa.case_number)\nWHERE ssa.subject_weapon = swa.subject_weapon;", + "sql_result_column_count": 6, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find the top 5 incidents where subjects ended up in poor shape and were involved with serious weapons, particularly those where the critical condition and assault weapon were matched together? I'd like to know the names and dates of those involved.", + "external_knowledge": "The \"MATCH\" operator in SQLite performs approximate nearest neighbor (ANN) search using vector embeddings. In this query, it is applied to find matches for \"Critical Condition\" and \"Assault Rifle,\" representing the semantic closeness in meaning. The `k=5` specifies that the query will return the top 5 records closest in meaning to these phrases. Vector similarity is calculated using the Euclidean distance and the closest matches indicate higher semantic similarity. Understanding domain knowledge, \"Critical Condition\" implies severe health status, while \"Assault Rifle\" denotes a military-grade weapon. This knowledge helps infer the serious nature of the incidents involved.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Critical Condition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Assault Rifle') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_statuses_embedding, ref_vec_0) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_weapon_embedding, ref_vec_1) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_status_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_statuses AS subject_statuses, \n i.subject_weapon AS subject_weapon,\n s.full_name AS subject_name,\n distance\n FROM i_filtered AS i\n JOIN subjects s ON toString(i.case_number) = toString(s.case_number)\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_weapon_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_weapon AS subject_weapon, \n i.subject_statuses AS subject_statuses,\n o.full_name AS officer_name,\n distance\n FROM i_filtered AS i\n JOIN officers o ON toString(i.case_number) = toString(o.case_number)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ssa.case_number AS case_number, \n ssa.date AS date, \n ssa.subject_name AS subject_name, \n swa.officer_name AS officer_name, \n ssa.subject_statuses AS subject_statuses, \n swa.subject_weapon AS subject_weapon\nFROM subject_status_analysis ssa\nJOIN subject_weapon_analysis swa ON toString(ssa.case_number) = toString(swa.case_number)\nWHERE ssa.subject_weapon = swa.subject_weapon;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Shotgun') AS ref_vec_0\n\nSELECT i.case_number, o.full_name, distance(i.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN officers o ON toString(i.case_number) = toString(o.case_number)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 15, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the five incidents where the weapon used by the subject is highly similar to a shotgun and provide the case numbers along with the full names of the officers involved.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Shotgun') AS ref_vec_0\n\nSELECT i.case_number, o.full_name, distance(i.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN officers o ON toString(i.case_number) = toString(o.case_number)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Deceased individual status') AS ref_vec_0\n\nSELECT i.case_number, s.full_name, distance(i.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN subjects s ON toString(i.case_number) = toString(s.case_number)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Identify the five closest encounters with the shadows of mortality. Who are the individuals involved, and how near do they stand to this somber threshold?", + "external_knowledge": "- The `MATCH` operator in vector operations is used for approximate nearest neighbor (ANN) searches, identifying the most similar items to a given vector.\n- The parameter `k = 5` indicates the query will return the 5 most relevant results.\n- Vector comparisons are based on Euclidean distances, where smaller distances denote greater similarity.\n- \"Deceased individual status\" refers to the state of subjects being recognized as deceased in the database context.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Deceased individual status') AS ref_vec_0\n\nSELECT i.case_number, s.full_name, distance(i.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN subjects s ON toString(i.case_number) = toString(s.case_number)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Firearm') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you find me the top 5 incidents where a firearm was involved? I'd love to know their case numbers and where they happened.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Firearm') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Injured') AS ref_vec_0\n\nSELECT case_number, date, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you provide the case numbers, dates, locations, and similarity distances for the 3 incidents most related to subjects being injured?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Injured') AS ref_vec_0\n\nSELECT case_number, date, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The subject is fleeing and posed a threat') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you identify a few incidents where the subjects were fleeing and seemed dangerous, and tell me where these incidents happened?", + "external_knowledge": "The `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search using the vector embeddings, which allows for the retrieval of items that are most similar in meaning to the provided description. The `lembed()` function is utilized with the embedding model `all-MiniLM-L6-v2` to convert textual descriptions into vector forms for comparison. The `k = 3` clause limits the results to the top 3 most similar incidents. In context, 'a few incidents' refers to these top 3 results based on vector similarity, where the subject is described as \"fleeing and posed a threat,\" implying urgency and danger.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The subject is fleeing and posed a threat') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);" + } +] \ No newline at end of file diff --git a/benchmark/data/results/bird/input_llm.json b/benchmark/data/results/bird/input_llm.json new file mode 100644 index 0000000..8a59c1f --- /dev/null +++ b/benchmark/data/results/bird/input_llm.json @@ -0,0 +1,882 @@ +[ + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding player performance in 2023') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top 3 players who had outstanding performances in 2023 and let me know what awards they got and when?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding player performance in 2023') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you find me the top 3 players who had outstanding performances in 2023 and let me know what awards they got and when?\n\nLet's think step by step!\n" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'example note content') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the top 5 players who have received awards with notes similar to \"example note content,\" and provide the awards and years they were received.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'example note content') AS ref_vec_0\n\nSELECT playerID, award, year, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 5 players who have received awards with notes similar to \"example note content,\" and provide the awards and years they were received.\n\nLet's think step by step!\n" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'achievement in sports') AS ref_vec_0\n\nSELECT playerID, award, distance(awards_players.note_embedding, ref_vec_0) AS distance \nFROM awards_players\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top player who's got an award for achievement in sports? I just need their player ID and the award they received.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'achievement in sports') AS ref_vec_0\n\nSELECT playerID, award, distance(awards_players.note_embedding, ref_vec_0) AS distance \nFROM awards_players\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you find me the top player who's got an award for achievement in sports? I just need their player ID and the award they received.\n\nLet's think step by step!\n" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'MVP award for outstanding performance') AS ref_vec_0,\n\nAwardedPlayers AS (\n SELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.award AS award,\n distance(ap.note_embedding, ref_vec_0) AS distance\n FROM \n awards_players ap\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.distance AS distance\nFROM \n AwardedPlayers ap\nJOIN \n player_allstar pa ON toString(ap.playerID) = toString(pa.playerID) AND ap.year = pa.season_id\nORDER BY \n ap.distance;", + "sql_result_column_count": 3, + "sql_result_rows_count": 6, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Who are the top 10 players recognized for MVP awards due to outstanding performance? Provide their ID, year, and similarity distance, considering their all-star participation, and order them by their relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'MVP award for outstanding performance') AS ref_vec_0,\n\nAwardedPlayers AS (\n SELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.award AS award,\n distance(ap.note_embedding, ref_vec_0) AS distance\n FROM \n awards_players ap\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n ap.playerID AS playerID,\n ap.year AS year,\n ap.distance AS distance\nFROM \n AwardedPlayers ap\nJOIN \n player_allstar pa ON toString(ap.playerID) = toString(pa.playerID) AND ap.year = pa.season_id\nORDER BY \n ap.distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWho are the top 10 players recognized for MVP awards due to outstanding performance? Provide their ID, year, and similarity distance, considering their all-star participation, and order them by their relevance.\n\nLet's think step by step!\n" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in championship games') AS ref_vec_0\n\nSELECT p.fullGivenName, ap.award, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Who are the top 5 players recognized for outstanding performance in championship games, and what awards did they receive?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in championship games') AS ref_vec_0\n\nSELECT p.fullGivenName, ap.award, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWho are the top 5 players recognized for outstanding performance in championship games, and what awards did they receive?\n\nLet's think step by step!\n" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional performance in the final game') AS ref_vec_0\n\nSELECT playerID, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Could you list the IDs of the top 5 players who were noted for their exceptional performance in the final game?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional performance in the final game') AS ref_vec_0\n\nSELECT playerID, distance(awards_players.note_embedding, ref_vec_0) AS distance\nFROM awards_players\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you list the IDs of the top 5 players who were noted for their exceptional performance in the final game?\n\nLet's think step by step!\n" + }, + { + "db_id": "professional_basketball", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The player exhibited exceptional performance') AS ref_vec_0\n\nSELECT p.fullGivenName, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Who are the five players that shine brightly with exceptional prowess and have stood out as stars in their field?", + "external_knowledge": "The `MATCH` operator with the `lembed()` function performs an approximate nearest neighbor search, which is used to find the most similar items based on vector embeddings. The `k=5` parameter specifies that the query should return the top 5 results. The embeddings are processed using Euclidean distance, where smaller distances imply higher similarity. The \"all-MiniLM-L6-v2\" model is designed to capture semantic meanings in vector space, allowing for nuanced comparison of textual descriptions such as \"exceptional performance.\"", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The player exhibited exceptional performance') AS ref_vec_0\n\nSELECT p.fullGivenName, distance(ap.note_embedding, ref_vec_0) AS distance\nFROM awards_players ap\nJOIN players p ON toString(ap.playerID) = toString(p.playerID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE awards_coaches (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `coachID` Nullable(String),\n `award` Nullable(String),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE awards_players (\n `playerID` Nullable(String),\n `award` Nullable(String),\n `year` Nullable(Int64),\n `lgID` Nullable(String),\n `note` Nullable(String),\n `pos` Nullable(String),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE coaches (\n `coachID` String,\n `year` Int64,\n `tmID` String,\n `lgID` Nullable(String),\n `stint` Int64,\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `post_wins` Nullable(Int64),\n `post_losses` Nullable(Int64)\n);\nCREATE TABLE draft (\n `id` Int64,\n `draftYear` Nullable(Int64),\n `draftRound` Nullable(Int64),\n `draftSelection` Nullable(Int64),\n `draftOverall` Nullable(Int64),\n `tmID` Nullable(String),\n `firstName` Nullable(String),\n `lastName` Nullable(String),\n `suffixName` Nullable(String),\n `playerID` Nullable(String),\n `draftFrom` Nullable(String),\n `lgID` Nullable(String)\n);\nCREATE TABLE player_allstar (\n `playerID` String,\n `last_name` Nullable(String),\n `first_name` Nullable(String),\n `season_id` Int64,\n `conference` Nullable(String),\n `league_id` Nullable(String),\n `games_played` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `o_rebounds` Nullable(Int64),\n `d_rebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `personal_fouls` Nullable(Int64),\n `fg_attempted` Nullable(Int64),\n `fg_made` Nullable(Int64),\n `ft_attempted` Nullable(Int64),\n `ft_made` Nullable(Int64),\n `three_attempted` Nullable(Int64),\n `three_made` Nullable(Int64)\n);\nCREATE TABLE players (\n `playerID` String,\n `useFirst` Nullable(String),\n `firstName` Nullable(String),\n `middleName` Nullable(String),\n `lastName` Nullable(String),\n `nameGiven` Nullable(String),\n `fullGivenName` Nullable(String),\n `nameSuffix` Nullable(String),\n `nameNick` Nullable(String),\n `pos` Nullable(String),\n `firstseason` Nullable(Int64),\n `lastseason` Nullable(Int64),\n `height` Nullable(Float64),\n `weight` Nullable(Int64),\n `college` Nullable(String),\n `collegeOther` Nullable(String),\n `birthDate` Nullable(Date),\n `birthCity` Nullable(String),\n `birthState` Nullable(String),\n `birthCountry` Nullable(String),\n `highSchool` Nullable(String),\n `hsCity` Nullable(String),\n `hsState` Nullable(String),\n `hsCountry` Nullable(String),\n `deathDate` Nullable(Date),\n `race` Nullable(String)\n);\nCREATE TABLE players_teams (\n `id` Nullable(Int64),\n `playerID` String,\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `tmID` Nullable(String),\n `lgID` Nullable(String),\n `GP` Nullable(Int64),\n `GS` Nullable(Int64),\n `minutes` Nullable(Int64),\n `points` Nullable(Int64),\n `oRebounds` Nullable(Int64),\n `dRebounds` Nullable(Int64),\n `rebounds` Nullable(Int64),\n `assists` Nullable(Int64),\n `steals` Nullable(Int64),\n `blocks` Nullable(Int64),\n `turnovers` Nullable(Int64),\n `PF` Nullable(Int64),\n `fgAttempted` Nullable(Int64),\n `fgMade` Nullable(Int64),\n `ftAttempted` Nullable(Int64),\n `ftMade` Nullable(Int64),\n `threeAttempted` Nullable(Int64),\n `threeMade` Nullable(Int64),\n `PostGP` Nullable(Int64),\n `PostGS` Nullable(Int64),\n `PostMinutes` Nullable(Int64),\n `PostPoints` Nullable(Int64),\n `PostoRebounds` Nullable(Int64),\n `PostdRebounds` Nullable(Int64),\n `PostRebounds` Nullable(Int64),\n `PostAssists` Nullable(Int64),\n `PostSteals` Nullable(Int64),\n `PostBlocks` Nullable(Int64),\n `PostTurnovers` Nullable(Int64),\n `PostPF` Nullable(Int64),\n `PostfgAttempted` Nullable(Int64),\n `PostfgMade` Nullable(Int64),\n `PostftAttempted` Nullable(Int64),\n `PostftMade` Nullable(Int64),\n `PostthreeAttempted` Nullable(Int64),\n `PostthreeMade` Nullable(Int64),\n `note` Nullable(String)\n);\nCREATE TABLE series_post (\n `id` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `series` Nullable(String),\n `tmIDWinner` Nullable(String),\n `lgIDWinner` Nullable(String),\n `tmIDLoser` Nullable(String),\n `lgIDLoser` Nullable(String),\n `W` Nullable(Int64),\n `L` Nullable(Int64)\n);\nCREATE TABLE teams (\n `year` Int64,\n `lgID` Nullable(String),\n `tmID` String,\n `franchID` Nullable(String),\n `confID` Nullable(String),\n `divID` Nullable(String),\n `rank` Nullable(Int64),\n `confRank` Nullable(Int64),\n `playoff` Nullable(String),\n `name` Nullable(String),\n `o_fgm` Nullable(Int64),\n `o_ftm` Nullable(Int64),\n `o_pts` Nullable(Int64),\n `d_pts` Nullable(Int64),\n `homeWon` Nullable(Int64),\n `homeLost` Nullable(Int64),\n `awayWon` Nullable(Int64),\n `awayLost` Nullable(Int64),\n `won` Nullable(Int64),\n `lost` Nullable(Int64),\n `games` Nullable(Int64),\n `arena` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator with the `lembed()` function performs an approximate nearest neighbor search, which is used to find the most similar items based on vector embeddings. The `k=5` parameter specifies that the query should return the top 5 results. The embeddings are processed using Euclidean distance, where smaller distances imply higher similarity. The \"all-MiniLM-L6-v2\" model is designed to capture semantic meanings in vector space, allowing for nuanced comparison of textual descriptions such as \"exceptional performance.\"\nWho are the five players that shine brightly with exceptional prowess and have stood out as stars in their field?\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "SELECT s.student_id\nFROM student s\nJOIN registration r ON toString(s.student_id) = toString(r.student_id)\nJOIN course c ON toString(r.course_id) = toString(c.course_id)\nWHERE s.intelligence > (\n SELECT AVG(intelligence)\n FROM student\n)\nAND s.gpa > 3.5\nAND c.diff > 3\nORDER BY s.intelligence DESC\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Who are the top five students with impressive intelligence and high GPA involved in challenging courses?", + "external_knowledge": "While this query doesn't involve vector operations, it focuses on filtering based on quantitative measures of intelligence and GPA, alongside course difficulty. Typically, in queries using vectors, operations might include similarity searches where the MATCH operator is used for approximate nearest neighbor searches. The \"k=N\" parameter limits the number of results based on similarity, with vectors compared by Euclidean distance. However, these are not applicable in the current SQL query context.", + "sql_candidate": [ + "SELECT s.student_id\nFROM student s\nJOIN registration r ON toString(s.student_id) = toString(r.student_id)\nJOIN course c ON toString(r.course_id) = toString(c.course_id)\nWHERE s.intelligence > (\n SELECT AVG(intelligence)\n FROM student\n)\nAND s.gpa > 3.5\nAND c.diff > 3\nORDER BY s.intelligence DESC\nLIMIT 5;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhile this query doesn't involve vector operations, it focuses on filtering based on quantitative measures of intelligence and GPA, alongside course difficulty. Typically, in queries using vectors, operations might include similarity searches where the MATCH operator is used for approximate nearest neighbor searches. The \"k=N\" parameter limits the number of results based on similarity, with vectors compared by Euclidean distance. However, these are not applicable in the current SQL query context.\nWho are the top five students with impressive intelligence and high GPA involved in challenging courses?\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Stanford University') AS ref_vec_0\n\nSELECT prof_id, distance(prof.graduate_from_embedding, ref_vec_0) AS distance \nFROM prof\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the professor who graduated from an institution most similar to Stanford University and provide their unique identifier.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Stanford University') AS ref_vec_0\n\nSELECT prof_id, distance(prof.graduate_from_embedding, ref_vec_0) AS distance \nFROM prof\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the professor who graduated from an institution most similar to Stanford University and provide their unique identifier.\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'PGS') AS ref_vec_0\n\nSELECT student_id, type, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify and list the student IDs and types for the top three postgraduate students according to their vector similarity classification.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'PGS') AS ref_vec_0\n\nSELECT student_id, type, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify and list the student IDs and types for the top three postgraduate students according to their vector similarity classification.\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'RPG player') AS ref_vec_0\n\nSELECT student_id, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Which student most closely relates to being an RPG player? Provide their ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'RPG player') AS ref_vec_0\n\nSELECT student_id, distance(student.type_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhich student most closely relates to being an RPG player? Provide their ID.\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'high salary for research assistants') AS ref_vec_0\n\nSELECT student_id, capability, distance(RA.salary_embedding, ref_vec_0) AS distance\nFROM RA\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the student IDs, their capabilities, and the similarity distances for the top 5 research assistants associated with high salaries?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'high salary for research assistants') AS ref_vec_0\n\nSELECT student_id, capability, distance(RA.salary_embedding, ref_vec_0) AS distance\nFROM RA\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the student IDs, their capabilities, and the similarity distances for the top 5 research assistants associated with high salaries?\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Harvurd Univ') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Scolar') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n WHERE popularity > 70\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(type_embedding, ref_vec_1) AS distance\n FROM student\n WHERE intelligence > 120\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.first_name AS professor_name,\n s.f_name AS student_name\nFROM p_filtered AS p\nJOIN\n RA r ON toString(p.prof_id) = toString(r.prof_id)\nJOIN s_filtered AS s ON toString(r.student_id) = toString(s.student_id)\nORDER BY \n p.popularity DESC, s.intelligence DESC\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Seek the brightest sparks in the academic galaxy: Who are the 10 shining stars, professors, and scholars, emerging from the esteemed halls akin to \"Harvurd Univ\", with professors basking in their glories above 70 in popularity, and scholars whose minds illuminate beyond 120 in intelligence?", + "external_knowledge": "The query leverages vector operations for approximate nearest neighbor (ANN) searches using the MATCH operator, which finds entities similar to specified concepts based on embeddings. The `k=5` clause indicates selecting the top 5 entities with the closest match for both professors and students. The embeddings are handled by the `lembed()` function using the model 'all-MiniLM-L6-v2', which assesses similarity based on Euclidean distance, where smaller distances indicate higher similarity. In this context, \"Harvurd Univ\" is associated with prestigious institutions, and \"Scholar\" signifies individuals dedicated to academic pursuits.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Harvurd Univ') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Scolar') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n WHERE popularity > 70\n ORDER BY distance\n LIMIT 5\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(type_embedding, ref_vec_1) AS distance\n FROM student\n WHERE intelligence > 120\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.first_name AS professor_name,\n s.f_name AS student_name\nFROM p_filtered AS p\nJOIN\n RA r ON toString(p.prof_id) = toString(r.prof_id)\nJOIN s_filtered AS s ON toString(r.student_id) = toString(s.student_id)\nORDER BY \n p.popularity DESC, s.intelligence DESC\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe query leverages vector operations for approximate nearest neighbor (ANN) searches using the MATCH operator, which finds entities similar to specified concepts based on embeddings. The `k=5` clause indicates selecting the top 5 entities with the closest match for both professors and students. The embeddings are handled by the `lembed()` function using the model 'all-MiniLM-L6-v2', which assesses similarity based on Euclidean distance, where smaller distances indicate higher similarity. In this context, \"Harvurd Univ\" is associated with prestigious institutions, and \"Scholar\" signifies individuals dedicated to academic pursuits.\nSeek the brightest sparks in the academic galaxy: Who are the 10 shining stars, professors, and scholars, emerging from the esteemed halls akin to \"Harvurd Univ\", with professors basking in their glories above 70 in popularity, and scholars whose minds illuminate beyond 120 in intelligence?\n\nLet's think step by step!\n" + }, + { + "db_id": "cs_semester", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Harvard University') AS ref_vec_0,\n\nHighSalaryRAs AS (\n SELECT student_id, prof_id\n FROM RA\n WHERE salary = 'high'\n),\n\nSimilarProfessors AS (\n SELECT prof_id, graduate_from, distance(prof.graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT s.f_name || ' ' || s.l_name AS student_name\nFROM student s\nJOIN HighSalaryRAs r ON toString(s.student_id) = toString(r.student_id)\nJOIN SimilarProfessors p ON toString(r.prof_id) = toString(p.prof_id)\nWHERE s.type = 'RA'\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of up to 10 Research Assistants with high salaries whose supervising professors graduated from one of the top 3 universities most similar to Harvard University?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Harvard University') AS ref_vec_0,\n\nHighSalaryRAs AS (\n SELECT student_id, prof_id\n FROM RA\n WHERE salary = 'high'\n),\n\nSimilarProfessors AS (\n SELECT prof_id, graduate_from, distance(prof.graduate_from_embedding, ref_vec_0) AS distance\n FROM prof\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT s.f_name || ' ' || s.l_name AS student_name\nFROM student s\nJOIN HighSalaryRAs r ON toString(s.student_id) = toString(r.student_id)\nJOIN SimilarProfessors p ON toString(r.prof_id) = toString(p.prof_id)\nWHERE s.type = 'RA'\nLIMIT 10;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE RA (\n `student_id` Nullable(Int64),\n `capability` Nullable(Int64),\n `prof_id` Nullable(Int64),\n `salary` Nullable(String),\n `salary_embedding` Array(Float32)\n);\nCREATE TABLE course (\n `course_id` Nullable(Int64),\n `name` Nullable(String),\n `credit` Nullable(Int64),\n `diff` Nullable(Int64)\n);\nCREATE TABLE prof (\n `prof_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `popularity` Nullable(Int64),\n `teachingability` Nullable(Int64),\n `graduate_from` Nullable(String),\n `graduate_from_embedding` Array(Float32)\n);\nCREATE TABLE registration (\n `course_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `grade` Nullable(String),\n `sat` Nullable(Int64)\n);\nCREATE TABLE student (\n `student_id` Nullable(Int64),\n `f_name` Nullable(String),\n `l_name` Nullable(String),\n `phone_number` Nullable(String),\n `email` Nullable(String),\n `intelligence` Nullable(Int64),\n `gpa` Nullable(Float64),\n `type` Nullable(String),\n `type_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the names of up to 10 Research Assistants with high salaries whose supervising professors graduated from one of the top 3 universities most similar to Harvard University?\n\nLet's think step by step!\n" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'brewery located in Portland, OR') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'India Pale Ale') AS ref_vec_1,\n\nbreweries_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbr_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 5\n),\n\nBreweryCTE AS (\n SELECT id, name, distance\n FROM breweries_filtered AS breweries\n)\n\nSELECT b.name AS brewery_name, br.name AS beer_name\nFROM BreweryCTE b\nJOIN br_filtered AS br ON toString(b.id) = toString(br.brewery_id)\nORDER BY br.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of the 5 breweries located in Portland, OR, along with the names of their top 5 India Pale Ale beers, ordered by similarity?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'brewery located in Portland, OR') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'India Pale Ale') AS ref_vec_1,\n\nbreweries_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbr_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 5\n),\n\nBreweryCTE AS (\n SELECT id, name, distance\n FROM breweries_filtered AS breweries\n)\n\nSELECT b.name AS brewery_name, br.name AS beer_name\nFROM BreweryCTE b\nJOIN br_filtered AS br ON toString(b.id) = toString(br.brewery_id)\nORDER BY br.distance;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the names of the 5 breweries located in Portland, OR, along with the names of their top 5 India Pale Ale beers, ordered by similarity?\n\nLet's think step by step!\n" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A brewery located in Portland, OR specializing in craft beers') AS ref_vec_0\n\nSELECT id, name, distance(breweries.breweries_description_embedding, ref_vec_0) AS distance\nFROM breweries\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the top 5 breweries located in Portland, OR specializing in craft beers. Provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A brewery located in Portland, OR specializing in craft beers') AS ref_vec_0\n\nSELECT id, name, distance(breweries.breweries_description_embedding, ref_vec_0) AS distance\nFROM breweries\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nFind the top 5 breweries located in Portland, OR specializing in craft beers. Provide their IDs and names.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'M Chinnaswamy Stadium is a venue located in the city with ID 1.') AS ref_vec_0\n\nSELECT Venue_Name, distance(Venue.Venue_description_embedding, ref_vec_0) AS distance\nFROM Venue\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the venue whose description most closely resembles the characteristics of \"M Chinnaswamy Stadium is a venue located in the city with ID 1\" and provide its name.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'M Chinnaswamy Stadium is a venue located in the city with ID 1.') AS ref_vec_0\n\nSELECT Venue_Name, distance(Venue.Venue_description_embedding, ref_vec_0) AS distance\nFROM Venue\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the venue whose description most closely resembles the characteristics of \"M Chinnaswamy Stadium is a venue located in the city with ID 1\" and provide its name.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional batsman known for his aggressive style and consistency.') AS ref_vec_0\n\nSELECT Player_Id, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Who is the player known for being an exceptional batsman with an aggressive style and consistency, and can you provide their unique identifier?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional batsman known for his aggressive style and consistency.') AS ref_vec_0\n\nSELECT Player_Id, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWho is the player known for being an exceptional batsman with an aggressive style and consistency, and can you provide their unique identifier?\n\nLet's think step by step!\n" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An American Pale Ale with rich malt flavor') AS ref_vec_0,\n\nfiltered_breweries AS (\n SELECT id, name\n FROM breweries\n WHERE state = 'MN'\n)\n\nSELECT b.name AS beer_name, br.name AS brewery_name, distance(b.beers_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN filtered_breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Uncover the top 5 enchanting brews from Minnesotan beer gardens that echo the tale of a rich malt melody found in an American Pale Ale.", + "external_knowledge": "The query utilizes vector search capabilities provided by sqlite-vec and sqlite-lembed extensions. The `MATCH` operator is a mechanism for performing an approximate nearest neighbor (ANN) search, which is used to identify items with similar characteristics or descriptions. Here, the `lembed(all-MiniLM-L6-v2, \"An American Pale Ale with rich malt flavor\")` function creates an embedding vector from the given description and compares it against existing beer description embeddings. By specifying `k = 5`, the query aims to find the top 5 beers closely aligning with the given description. The similarity is measured by Euclidean distance, and results with smaller distances are considered more similar, thus ranked higher.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An American Pale Ale with rich malt flavor') AS ref_vec_0,\n\nfiltered_breweries AS (\n SELECT id, name\n FROM breweries\n WHERE state = 'MN'\n)\n\nSELECT b.name AS beer_name, br.name AS brewery_name, distance(b.beers_description_embedding, ref_vec_0) AS distance\nFROM beers b\nJOIN filtered_breweries br ON toString(b.brewery_id) = toString(br.id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe query utilizes vector search capabilities provided by sqlite-vec and sqlite-lembed extensions. The `MATCH` operator is a mechanism for performing an approximate nearest neighbor (ANN) search, which is used to identify items with similar characteristics or descriptions. Here, the `lembed(all-MiniLM-L6-v2, \"An American Pale Ale with rich malt flavor\")` function creates an embedding vector from the given description and compares it against existing beer description embeddings. By specifying `k = 5`, the query aims to find the top 5 beers closely aligning with the given description. The similarity is measured by Euclidean distance, and results with smaller distances are considered more similar, thus ranked higher.\nUncover the top 5 enchanting brews from Minnesotan beer gardens that echo the tale of a rich malt melody found in an American Pale Ale.\n\nLet's think step by step!\n" + }, + { + "db_id": "craftbeer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned brewery located in Denver, CO') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A popular American IPA with a high IBU score brewed at brewery in Denver') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbe_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 10\n),\n\nbrewery_matches AS (\n SELECT b.id as brewery_id, b.name, b.city, b.state, distance as brewery_distance\n FROM b_filtered AS b\n),\n\nbeer_matches AS (\n SELECT br.brewery_id, be.id as beer_id, be.name, be.style, be.abv, be.ibu, be.ounces, distance as beer_distance\n FROM be_filtered AS be\n JOIN brewery_matches br ON toString(be.brewery_id) = toString(br.brewery_id)\n)\n\nSELECT bm.beer_id\nFROM beer_matches bm\nORDER BY bm.beer_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Please identify the single beer that is most closely associated with being a popular American IPA with a high IBU score brewed at a brewery located in Denver, CO. Could you return its ID for me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned brewery located in Denver, CO') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A popular American IPA with a high IBU score brewed at brewery in Denver') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(breweries_description_embedding, ref_vec_0) AS distance\n FROM breweries\n\n ORDER BY distance\n LIMIT 5\n),\n\nbe_filtered AS (\n SELECT\n *,\n distance(beers_description_embedding, ref_vec_1) AS distance\n FROM beers\n\n ORDER BY distance\n LIMIT 10\n),\n\nbrewery_matches AS (\n SELECT b.id as brewery_id, b.name, b.city, b.state, distance as brewery_distance\n FROM b_filtered AS b\n),\n\nbeer_matches AS (\n SELECT br.brewery_id, be.id as beer_id, be.name, be.style, be.abv, be.ibu, be.ounces, distance as beer_distance\n FROM be_filtered AS be\n JOIN brewery_matches br ON toString(be.brewery_id) = toString(br.brewery_id)\n)\n\nSELECT bm.beer_id\nFROM beer_matches bm\nORDER BY bm.beer_distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE beers (\n `id` Nullable(Int64),\n `brewery_id` Nullable(Int64),\n `abv` Nullable(Float64),\n `ibu` Nullable(Float64),\n `name` Nullable(String),\n `style` Nullable(String),\n `ounces` Nullable(Float64),\n `beers_description` Nullable(String),\n `beers_description_embedding` Array(Float32)\n);\nCREATE TABLE breweries (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `breweries_description` Nullable(String),\n `breweries_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease identify the single beer that is most closely associated with being a popular American IPA with a high IBU score brewed at a brewery located in Denver, CO. Could you return its ID for me?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A right-handed batsman from England known for his aggressive style.') AS ref_vec_0,\n\nPlayerVectorSearch AS (\n SELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT Player_Name\nFROM PlayerVectorSearch;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "What is the name of the player who is a top match for being described as a right-handed batsman from England known for his aggressive style?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A right-handed batsman from England known for his aggressive style.') AS ref_vec_0,\n\nPlayerVectorSearch AS (\n SELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT Player_Name\nFROM PlayerVectorSearch;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat is the name of the player who is a top match for being described as a right-handed batsman from England known for his aggressive style?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its sports events') AS ref_vec_0\n\nSELECT v.Venue_Name, distance(c.City_description_embedding, ref_vec_0) AS distance\nFROM Venue v\nJOIN City c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "**\n\nPlease find the names of venues located in the top 3 cities that are vibrant and known for their sports events.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its sports events') AS ref_vec_0\n\nSELECT v.Venue_Name, distance(c.City_description_embedding, ref_vec_0) AS distance\nFROM Venue v\nJOIN City c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n**\n\nPlease find the names of venues located in the top 3 cities that are vibrant and known for their sports events.\n\n**\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A skilled batsman with a consistent performance record and recognized for an outstanding play.') AS ref_vec_0\n\nSELECT m.Match_Date, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Match m ON toString(p.Player_Id) = toString(m.Man_of_the_Match)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you tell me the dates of matches where the top 3 players, known for being skilled batsmen with consistent performance and recognized for outstanding play, were named Man of the Match?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A skilled batsman with a consistent performance record and recognized for an outstanding play.') AS ref_vec_0\n\nSELECT m.Match_Date, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Match m ON toString(p.Player_Id) = toString(m.Man_of_the_Match)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCan you tell me the dates of matches where the top 3 players, known for being skilled batsmen with consistent performance and recognized for outstanding play, were named Man of the Match?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned stadium known for hosting international matches') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A vibrant city with historical significance in sports') AS ref_vec_1,\n\nv_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 3\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(City_description_embedding, ref_vec_1) AS distance\n FROM City\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT v.Venue_Name\nFROM v_filtered AS v\nJOIN c_filtered AS c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY v.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the venue that’s like a top-notch stadium famous for international games and is in a lively city with a rich sports history? I just need the name of that venue.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned stadium known for hosting international matches') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A vibrant city with historical significance in sports') AS ref_vec_1,\n\nv_filtered AS (\n SELECT\n *,\n distance(Venue_description_embedding, ref_vec_0) AS distance\n FROM Venue\n\n ORDER BY distance\n LIMIT 3\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(City_description_embedding, ref_vec_1) AS distance\n FROM City\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT v.Venue_Name\nFROM v_filtered AS v\nJOIN c_filtered AS c ON toString(v.City_Id) = toString(c.City_Id)\nORDER BY v.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you help me find the venue that’s like a top-notch stadium famous for international games and is in a lively city with a rich sports history? I just need the name of that venue.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its historical sights and cultural heritage') AS ref_vec_0\n\nSELECT City_Id, City_Name, distance(City.City_description_embedding, ref_vec_0) AS distance \nFROM City\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which city is most recognized for its vibrant historical sights and cultural heritage?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its historical sights and cultural heritage') AS ref_vec_0\n\nSELECT City_Id, City_Name, distance(City.City_description_embedding, ref_vec_0) AS distance \nFROM City\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me which city is most recognized for its vibrant historical sights and cultural heritage?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional cricket player with outstanding skills') AS ref_vec_0\n\nSELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Who are the top 3 cricket players known for their exceptional skills?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exceptional cricket player with outstanding skills') AS ref_vec_0\n\nSELECT Player_Id, Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance \nFROM Player\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWho are the top 3 cricket players known for their exceptional skills?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A talented player known for exceptional performance under pressure') AS ref_vec_0,\n\nSimilarPlayers AS (\n SELECT Player_Id, Player_Name, Country_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n sp.Player_Name AS Player_Name,\n t.Team_Name AS Team_Name,\n m.Match_Date AS Match_Date,\n m.Win_Margin AS Win_Margin\nFROM\n SimilarPlayers sp\nJOIN\n Player_Match pm ON toString(sp.Player_Id) = toString(pm.Player_Id)\nJOIN\n Match m ON toString(pm.Match_Id) = toString(m.Match_Id)\nJOIN\n Team t ON toString(m.Match_Winner) = toString(t.Team_Id)\nWHERE\n m.Win_Margin > 50\nORDER BY\n m.Win_Margin DESC\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I am interested in finding the top 5 matches where players, who are known for their exceptional performance under pressure, played and won with a margin greater than 50. Please provide the names of these players, their team names, the dates of the matches, and the win margins, sorted by the win margins in descending order.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A talented player known for exceptional performance under pressure') AS ref_vec_0,\n\nSimilarPlayers AS (\n SELECT Player_Id, Player_Name, Country_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\n FROM Player\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT \n sp.Player_Name AS Player_Name,\n t.Team_Name AS Team_Name,\n m.Match_Date AS Match_Date,\n m.Win_Margin AS Win_Margin\nFROM\n SimilarPlayers sp\nJOIN\n Player_Match pm ON toString(sp.Player_Id) = toString(pm.Player_Id)\nJOIN\n Match m ON toString(pm.Match_Id) = toString(m.Match_Id)\nJOIN\n Team t ON toString(m.Match_Winner) = toString(t.Team_Id)\nWHERE\n m.Win_Margin > 50\nORDER BY\n m.Win_Margin DESC\nLIMIT 5;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI am interested in finding the top 5 matches where players, who are known for their exceptional performance under pressure, played and won with a margin greater than 50. Please provide the names of these players, their team names, the dates of the matches, and the win margins, sorted by the win margins in descending order.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis with a rich history and vibrant culture') AS ref_vec_0\n\nSELECT City_Id, City_Name, Country_id, City_description, distance(City.City_description_embedding, ref_vec_0) AS distance\nFROM City\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 5, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the vast tapestry of cities, which one stands as the vibrant epicenter of history and culture, echoing the lively spirit of a bustling metropolis? Reveal its identity, name, belonging country, and the measure of its proximity to this spirited essence.", + "external_knowledge": "- The `MATCH` operator in the query is designed to find vector embeddings that are most similar to a specified text concept, in this case, using the \"all-MiniLM-L6-v2\" model.\n- The vector search performed is an approximate nearest neighbor (ANN) search, which efficiently identifies items that are closest in vector space.\n- Euclidean distance (L2 norm) is typically used to determine similarity, where a smaller distance indicates higher similarity.\n- The concept of \"A bustling metropolis with a rich history and vibrant culture\" serves as the semantic benchmark for comparison.\n- The `LIMIT 1` clause ensures that only the city most closely matching this concept is returned.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis with a rich history and vibrant culture') AS ref_vec_0\n\nSELECT City_Id, City_Name, Country_id, City_description, distance(City.City_description_embedding, ref_vec_0) AS distance\nFROM City\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator in the query is designed to find vector embeddings that are most similar to a specified text concept, in this case, using the \"all-MiniLM-L6-v2\" model.\n- The vector search performed is an approximate nearest neighbor (ANN) search, which efficiently identifies items that are closest in vector space.\n- Euclidean distance (L2 norm) is typically used to determine similarity, where a smaller distance indicates higher similarity.\n- The concept of \"A bustling metropolis with a rich history and vibrant culture\" serves as the semantic benchmark for comparison.\n- The `LIMIT 1` clause ensures that only the city most closely matching this concept is returned.\nIn the vast tapestry of cities, which one stands as the vibrant epicenter of history and culture, echoing the lively spirit of a bustling metropolis? Reveal its identity, name, belonging country, and the measure of its proximity to this spirited essence.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A cricket player from Australia known for left-handed batting') AS ref_vec_0\n\nSELECT p.Player_Name, p.Batting_hand, distance(p.Player_description_embedding, ref_vec_0) AS distance \nFROM Player p\nJOIN Batting_Style bs ON toString(p.Batting_hand) = toString(bs.Batting_Id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the top 3 cricket players from the database who are most like an Australian player known for left-handed batting, including their names, batting styles, and how closely they match this description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A cricket player from Australia known for left-handed batting') AS ref_vec_0\n\nSELECT p.Player_Name, p.Batting_hand, distance(p.Player_description_embedding, ref_vec_0) AS distance \nFROM Player p\nJOIN Batting_Style bs ON toString(p.Batting_hand) = toString(bs.Batting_Id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the top 3 cricket players from the database who are most like an Australian player known for left-handed batting, including their names, batting styles, and how closely they match this description?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned cricketer from Australia known for his aggressive batting style') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the cricketer from Australia who's famous for his aggressive batting style? I'm looking for just one name that really fits that description.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned cricketer from Australia known for his aggressive batting style') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you help me find the cricketer from Australia who's famous for his aggressive batting style? I'm looking for just one name that really fits that description.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned Indian cricketer known for exceptional skill') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I would like to know the name of the player who is most recognized as an exceptional Indian cricketer.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned Indian cricketer known for exceptional skill') AS ref_vec_0\n\nSELECT Player_Name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI would like to know the name of the player who is most recognized as an exceptional Indian cricketer.\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2016", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An excellent cricket player with outstanding batting skills') AS ref_vec_0\n\nSELECT p.Player_Name, c.Country_Name, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Country c ON toString(p.Country_Name) = toString(c.Country_Id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you tell me the names and countries of the top five cricket players who are known for being truly outstanding with their batting skills?", + "external_knowledge": "Vector searches using the 'MATCH' operator perform an approximate nearest neighbor (ANN) search, which compares vector embeddings based on semantic similarity. In this query, the 'lembed' function is employed with the 'all-MiniLM-L6-v2' model to find players whose descriptions are semantically similar to \"An excellent cricket player with outstanding batting skills\". The parameter 'k=5' specifies that the search is limited to the top 5 players, with similarity determined by proximity in the vector space. These searches help identify entities that are conceptually close to a specified description, leveraging the power of natural language processing models to interpret and rank the results.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An excellent cricket player with outstanding batting skills') AS ref_vec_0\n\nSELECT p.Player_Name, c.Country_Name, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Country c ON toString(p.Country_Name) = toString(c.Country_Id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Ball_by_Ball (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Innings_No` Nullable(Int64),\n `Team_Batting` Nullable(Int64),\n `Team_Bowling` Nullable(Int64),\n `Striker_Batting_Position` Nullable(Int64),\n `Striker` Nullable(Int64),\n `Non_Striker` Nullable(Int64),\n `Bowler` Nullable(Int64)\n);\nCREATE TABLE Batsman_Scored (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Runs_Scored` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Batting_Style (\n `Batting_Id` Nullable(Int64),\n `Batting_hand` Nullable(String)\n);\nCREATE TABLE Bowling_Style (\n `Bowling_Id` Nullable(Int64),\n `Bowling_skill` Nullable(String)\n);\nCREATE TABLE City (\n `City_Id` Nullable(Int64),\n `City_Name` Nullable(String),\n `Country_id` Nullable(Int64),\n `City_description` Nullable(String),\n `City_description_embedding` Array(Float32)\n);\nCREATE TABLE Country (\n `Country_Id` Nullable(Int64),\n `Country_Name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE Extra_Runs (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Extra_Type_Id` Nullable(Int64),\n `Extra_Runs` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Extra_Type (\n `Extra_Id` Nullable(Int64),\n `Extra_Name` Nullable(String)\n);\nCREATE TABLE Match (\n `Match_Id` Nullable(Int64),\n `Team_1` Nullable(Int64),\n `Team_2` Nullable(Int64),\n `Match_Date` Nullable(Date),\n `Season_Id` Nullable(Int64),\n `Venue_Id` Nullable(Int64),\n `Toss_Winner` Nullable(Int64),\n `Toss_Decide` Nullable(Int64),\n `Win_Type` Nullable(Int64),\n `Win_Margin` Nullable(Int64),\n `Outcome_type` Nullable(Int64),\n `Match_Winner` Nullable(Int64),\n `Man_of_the_Match` Nullable(Int64)\n);\nCREATE TABLE Out_Type (\n `Out_Id` Nullable(Int64),\n `Out_Name` Nullable(String)\n);\nCREATE TABLE Outcome (\n `Outcome_Id` Nullable(Int64),\n `Outcome_Type` Nullable(String)\n);\nCREATE TABLE Player (\n `Player_Id` Nullable(Int64),\n `Player_Name` Nullable(String),\n `DOB` Nullable(String),\n `Batting_hand` Nullable(Int64),\n `Bowling_skill` Nullable(Int64),\n `Country_Name` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Match (\n `Match_Id` Nullable(Int64),\n `Player_Id` Nullable(Int64),\n `Role_Id` Nullable(Int64),\n `Team_Id` Nullable(Int64)\n);\nCREATE TABLE Rolee (\n `Role_Id` Nullable(Int64),\n `Role_Desc` Nullable(String),\n `Role_Desc_embedding` Array(Float32)\n);\nCREATE TABLE Season (\n `Season_Id` Nullable(Int64),\n `Man_of_the_Series` Nullable(Int64),\n `Orange_Cap` Nullable(Int64),\n `Purple_Cap` Nullable(Int64),\n `Season_Year` Nullable(Int64),\n `Season_description` Nullable(String),\n `Season_description_embedding` Array(Float32)\n);\nCREATE TABLE Team (\n `Team_Id` Nullable(Int64),\n `Team_Name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Toss_Decision (\n `Toss_Id` Nullable(Int64),\n `Toss_Name` Nullable(String)\n);\nCREATE TABLE Umpire (\n `Umpire_Id` Nullable(Int64),\n `Umpire_Name` Nullable(String),\n `Umpire_Country` Nullable(Int64),\n `Umpire_description` Nullable(String),\n `Umpire_description_embedding` Array(Float32)\n);\nCREATE TABLE Venue (\n `Venue_Id` Nullable(Int64),\n `Venue_Name` Nullable(String),\n `City_Id` Nullable(Int64),\n `Venue_description` Nullable(String),\n `Venue_description_embedding` Array(Float32)\n);\nCREATE TABLE Wicket_Taken (\n `Match_Id` Nullable(Int64),\n `Over_Id` Nullable(Int64),\n `Ball_Id` Nullable(Int64),\n `Player_Out` Nullable(Int64),\n `Kind_Out` Nullable(Int64),\n `Fielders` Nullable(Int64),\n `Innings_No` Nullable(Int64)\n);\nCREATE TABLE Win_By (\n `Win_Id` Nullable(Int64),\n `Win_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nVector searches using the 'MATCH' operator perform an approximate nearest neighbor (ANN) search, which compares vector embeddings based on semantic similarity. In this query, the 'lembed' function is employed with the 'all-MiniLM-L6-v2' model to find players whose descriptions are semantically similar to \"An excellent cricket player with outstanding batting skills\". The parameter 'k=5' specifies that the search is limited to the top 5 players, with similarity determined by proximity in the vector space. These searches help identify entities that are conceptually close to a specified description, leveraging the power of natural language processing models to interpret and rank the results.\nCan you tell me the names and countries of the top five cricket players who are known for being truly outstanding with their batting skills?\n\nLet's think step by step!\n" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0\n\nSELECT SurveyID, Description, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "What are three surveys that seem to deal with the 2020 mental health topic?", + "external_knowledge": "- The `MATCH` operator performs approximate nearest neighbor (ANN) search, allowing retrieval of items similar to a given vector.\n- The `k = 3` parameter limits the results to the top three most similar items according to the vector similarity search.\n- Vector embeddings are produced using the \"all-MiniLM-L6-v2\" model, which is a transformer model designed for handling semantic similarity and sentence embeddings.\n- The similarity between vectors typically uses Euclidean distance (L2 norm) by default; surveys with smaller distances are considered more semantically similar to the search phrase.\n- Understanding the domain context: \"mental health survey for 2020\" implies surveys related to mental health conducted in the year 2020.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0\n\nSELECT SurveyID, Description, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator performs approximate nearest neighbor (ANN) search, allowing retrieval of items similar to a given vector.\n- The `k = 3` parameter limits the results to the top three most similar items according to the vector similarity search.\n- Vector embeddings are produced using the \"all-MiniLM-L6-v2\" model, which is a transformer model designed for handling semantic similarity and sentence embeddings.\n- The similarity between vectors typically uses Euclidean distance (L2 norm) by default; surveys with smaller distances are considered more semantically similar to the search phrase.\n- Understanding the domain context: \"mental health survey for 2020\" implies surveys related to mental health conducted in the year 2020.\nWhat are three surveys that seem to deal with the 2020 mental health topic?\n\nLet's think step by step!\n" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0,\n\nSimilarSurveys AS (\n SELECT \n SurveyID, \n Description, \n distance(Survey.Description_embedding, ref_vec_0) AS distance\n FROM \n Survey\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n Description\nFROM \n SimilarSurveys;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Can you provide the description of the survey that best matches the topic of \"mental health survey for 2020\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey for 2020') AS ref_vec_0,\n\nSimilarSurveys AS (\n SELECT \n SurveyID, \n Description, \n distance(Survey.Description_embedding, ref_vec_0) AS distance\n FROM \n Survey\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n Description\nFROM \n SimilarSurveys;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the description of the survey that best matches the topic of \"mental health survey for 2020\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey report for the recent year') AS ref_vec_0\n\nSELECT SurveyID, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the top 3 surveys related to a recent mental health survey report? Please provide their IDs and similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'mental health survey report for the recent year') AS ref_vec_0\n\nSELECT SurveyID, distance(Survey.Description_embedding, ref_vec_0) AS distance\nFROM Survey\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat are the top 3 surveys related to a recent mental health survey report? Please provide their IDs and similarity distances.\n\nLet's think step by step!\n" + }, + { + "db_id": "mental_health_survey", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'What is your profession?') AS ref_vec_0\n\nSELECT questionid, distance(Question.questiontext_embedding, ref_vec_0) AS distance\nFROM Question\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID of the question most related to \"What is your profession?\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'What is your profession?') AS ref_vec_0\n\nSELECT questionid, distance(Question.questiontext_embedding, ref_vec_0) AS distance\nFROM Question\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Answer (\n `AnswerText` Nullable(String),\n `SurveyID` Nullable(Int64),\n `UserID` Nullable(Int64),\n `QuestionID` Nullable(Int64)\n);\nCREATE TABLE Question (\n `questiontext` Nullable(String),\n `questionid` Nullable(Int64),\n `questiontext_embedding` Array(Float32)\n);\nCREATE TABLE Survey (\n `SurveyID` Nullable(Int64),\n `Description` Nullable(String),\n `Description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat is the ID of the question most related to \"What is your profession?\"\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Knife or blade weapon') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the case numbers and locations for the top 5 incidents involving knife or blade weapons?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Knife or blade weapon') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the case numbers and locations for the top 5 incidents involving knife or blade weapons?\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Knife') AS ref_vec_0\n\nSELECT case_number, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the three incidents where the weapon used is most representative of a knife, and provide me with their case numbers?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Knife') AS ref_vec_0\n\nSELECT case_number, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the three incidents where the weapon used is most representative of a knife, and provide me with their case numbers?\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Critical Condition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Assault Rifle') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_statuses_embedding, ref_vec_0) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_weapon_embedding, ref_vec_1) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_status_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_statuses AS subject_statuses, \n i.subject_weapon AS subject_weapon,\n s.full_name AS subject_name,\n distance\n FROM i_filtered AS i\n JOIN subjects s ON toString(i.case_number) = toString(s.case_number)\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_weapon_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_weapon AS subject_weapon, \n i.subject_statuses AS subject_statuses,\n o.full_name AS officer_name,\n distance\n FROM i_filtered AS i\n JOIN officers o ON toString(i.case_number) = toString(o.case_number)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ssa.case_number AS case_number, \n ssa.date AS date, \n ssa.subject_name AS subject_name, \n swa.officer_name AS officer_name, \n ssa.subject_statuses AS subject_statuses, \n swa.subject_weapon AS subject_weapon\nFROM subject_status_analysis ssa\nJOIN subject_weapon_analysis swa ON toString(ssa.case_number) = toString(swa.case_number)\nWHERE ssa.subject_weapon = swa.subject_weapon;", + "sql_result_column_count": 6, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find the top 5 incidents where subjects ended up in poor shape and were involved with serious weapons, particularly those where the critical condition and assault weapon were matched together? I'd like to know the names and dates of those involved.", + "external_knowledge": "The \"MATCH\" operator in SQLite performs approximate nearest neighbor (ANN) search using vector embeddings. In this query, it is applied to find matches for \"Critical Condition\" and \"Assault Rifle,\" representing the semantic closeness in meaning. The `k=5` specifies that the query will return the top 5 records closest in meaning to these phrases. Vector similarity is calculated using the Euclidean distance and the closest matches indicate higher semantic similarity. Understanding domain knowledge, \"Critical Condition\" implies severe health status, while \"Assault Rifle\" denotes a military-grade weapon. This knowledge helps infer the serious nature of the incidents involved.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Critical Condition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Assault Rifle') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_statuses_embedding, ref_vec_0) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(subject_weapon_embedding, ref_vec_1) AS distance\n FROM incidents\n\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_status_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_statuses AS subject_statuses, \n i.subject_weapon AS subject_weapon,\n s.full_name AS subject_name,\n distance\n FROM i_filtered AS i\n JOIN subjects s ON toString(i.case_number) = toString(s.case_number)\n ORDER BY distance\n LIMIT 5\n),\n\nsubject_weapon_analysis AS (\n SELECT \n i.case_number AS case_number, \n i.date AS date, \n i.subject_weapon AS subject_weapon, \n i.subject_statuses AS subject_statuses,\n o.full_name AS officer_name,\n distance\n FROM i_filtered AS i\n JOIN officers o ON toString(i.case_number) = toString(o.case_number)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ssa.case_number AS case_number, \n ssa.date AS date, \n ssa.subject_name AS subject_name, \n swa.officer_name AS officer_name, \n ssa.subject_statuses AS subject_statuses, \n swa.subject_weapon AS subject_weapon\nFROM subject_status_analysis ssa\nJOIN subject_weapon_analysis swa ON toString(ssa.case_number) = toString(swa.case_number)\nWHERE ssa.subject_weapon = swa.subject_weapon;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe \"MATCH\" operator in SQLite performs approximate nearest neighbor (ANN) search using vector embeddings. In this query, it is applied to find matches for \"Critical Condition\" and \"Assault Rifle,\" representing the semantic closeness in meaning. The `k=5` specifies that the query will return the top 5 records closest in meaning to these phrases. Vector similarity is calculated using the Euclidean distance and the closest matches indicate higher semantic similarity. Understanding domain knowledge, \"Critical Condition\" implies severe health status, while \"Assault Rifle\" denotes a military-grade weapon. This knowledge helps infer the serious nature of the incidents involved.\nCan you find the top 5 incidents where subjects ended up in poor shape and were involved with serious weapons, particularly those where the critical condition and assault weapon were matched together? I'd like to know the names and dates of those involved.\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Shotgun') AS ref_vec_0\n\nSELECT i.case_number, o.full_name, distance(i.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN officers o ON toString(i.case_number) = toString(o.case_number)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 15, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the five incidents where the weapon used by the subject is highly similar to a shotgun and provide the case numbers along with the full names of the officers involved.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Shotgun') AS ref_vec_0\n\nSELECT i.case_number, o.full_name, distance(i.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN officers o ON toString(i.case_number) = toString(o.case_number)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the five incidents where the weapon used by the subject is highly similar to a shotgun and provide the case numbers along with the full names of the officers involved.\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Deceased individual status') AS ref_vec_0\n\nSELECT i.case_number, s.full_name, distance(i.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN subjects s ON toString(i.case_number) = toString(s.case_number)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Identify the five closest encounters with the shadows of mortality. Who are the individuals involved, and how near do they stand to this somber threshold?", + "external_knowledge": "- The `MATCH` operator in vector operations is used for approximate nearest neighbor (ANN) searches, identifying the most similar items to a given vector.\n- The parameter `k = 5` indicates the query will return the 5 most relevant results.\n- Vector comparisons are based on Euclidean distances, where smaller distances denote greater similarity.\n- \"Deceased individual status\" refers to the state of subjects being recognized as deceased in the database context.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Deceased individual status') AS ref_vec_0\n\nSELECT i.case_number, s.full_name, distance(i.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents i\nJOIN subjects s ON toString(i.case_number) = toString(s.case_number)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator in vector operations is used for approximate nearest neighbor (ANN) searches, identifying the most similar items to a given vector.\n- The parameter `k = 5` indicates the query will return the 5 most relevant results.\n- Vector comparisons are based on Euclidean distances, where smaller distances denote greater similarity.\n- \"Deceased individual status\" refers to the state of subjects being recognized as deceased in the database context.\nIdentify the five closest encounters with the shadows of mortality. Who are the individuals involved, and how near do they stand to this somber threshold?\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Firearm') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you find me the top 5 incidents where a firearm was involved? I'd love to know their case numbers and where they happened.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Firearm') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_weapon_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you find me the top 5 incidents where a firearm was involved? I'd love to know their case numbers and where they happened.\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Injured') AS ref_vec_0\n\nSELECT case_number, date, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you provide the case numbers, dates, locations, and similarity distances for the 3 incidents most related to subjects being injured?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Injured') AS ref_vec_0\n\nSELECT case_number, date, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you provide the case numbers, dates, locations, and similarity distances for the 3 incidents most related to subjects being injured?\n\nLet's think step by step!\n" + }, + { + "db_id": "shooting", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The subject is fleeing and posed a threat') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you identify a few incidents where the subjects were fleeing and seemed dangerous, and tell me where these incidents happened?", + "external_knowledge": "The `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search using the vector embeddings, which allows for the retrieval of items that are most similar in meaning to the provided description. The `lembed()` function is utilized with the embedding model `all-MiniLM-L6-v2` to convert textual descriptions into vector forms for comparison. The `k = 3` clause limits the results to the top 3 most similar incidents. In context, 'a few incidents' refers to these top 3 results based on vector similarity, where the subject is described as \"fleeing and posed a threat,\" implying urgency and danger.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The subject is fleeing and posed a threat') AS ref_vec_0\n\nSELECT case_number, location, distance(incidents.subject_statuses_embedding, ref_vec_0) AS distance\nFROM incidents\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE incidents (\n `case_number` Nullable(String),\n `date` Nullable(String),\n `location` Nullable(String),\n `subject_statuses` Nullable(String),\n `subject_weapon` Nullable(String),\n `subjects` Nullable(String),\n `subject_count` Nullable(Int64),\n `officers` Nullable(String),\n `subject_statuses_embedding` Array(Float32),\n `subject_weapon_embedding` Array(Float32)\n);\nCREATE TABLE officers (\n `case_number` String,\n `race` Nullable(String),\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\nCREATE TABLE subjects (\n `case_number` String,\n `race` String,\n `gender` String,\n `last_name` String,\n `first_name` Nullable(String),\n `full_name` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search using the vector embeddings, which allows for the retrieval of items that are most similar in meaning to the provided description. The `lembed()` function is utilized with the embedding model `all-MiniLM-L6-v2` to convert textual descriptions into vector forms for comparison. The `k = 3` clause limits the results to the top 3 most similar incidents. In context, 'a few incidents' refers to these top 3 results based on vector similarity, where the subject is described as \"fleeing and posed a threat,\" implying urgency and danger.\nCould you identify a few incidents where the subjects were fleeing and seemed dangerous, and tell me where these incidents happened?\n\nLet's think step by step!\n" + } +] \ No newline at end of file diff --git a/benchmark/data/results/spider/candidate_sql.json b/benchmark/data/results/spider/candidate_sql.json new file mode 100644 index 0000000..0426f1f --- /dev/null +++ b/benchmark/data/results/spider/candidate_sql.json @@ -0,0 +1,3458 @@ +[ + { + "db_id": "party_people", + "sql": "SELECT r.Region_name\nFROM region r\nJOIN party p ON r.Region_ID = p.Region_ID\nWHERE r.region_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"The strategic and economic significance of the region has been pivotal in its development.\") AND r.k = 10\nAND p.party_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"A political party known for its emphasis on social reform and economic development.\") AND p.k = 10\nORDER BY (SELECT MIN(distance) FROM (SELECT distance FROM region WHERE region_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"The strategic and economic significance of the region has been pivotal in its development.\") LIMIT 10)\n UNION\n SELECT MIN(distance) FROM (SELECT distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"A political party known for its emphasis on social reform and economic development.\") LIMIT 10));", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the top 10 regions that play a significant strategic and economic role in their development, and are associated with political parties known for their focus on social reform and economic development. List these regions in order of their relevance to the described themes.", + "external_knowledge": "", + "sql_candidate": [ + "SELECT r.Region_name FROM region r JOIN party p ON r.Region_ID = p.Region_ID WHERE r.region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Regions with significant strategic and economic influence in their development.') AND r.k = 10 AND p.party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Political parties focused on social change and economic growth.') AND p.k = 10 ORDER BY (SELECT MIN(distance) FROM (SELECT distance FROM region WHERE region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Regions with significant strategic and economic influence in their development.') LIMIT 10) UNION SELECT MIN(distance) FROM (SELECT distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Political parties focused on social change and economic growth.') LIMIT 10));", + "SELECT r.Region_name FROM region r JOIN party p ON r.Region_ID = p.Region_ID WHERE r.region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Areas playing a key role in economic and strategic development.') AND r.k = 10 AND p.party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Parties advocating for social reforms and economic progress.') AND p.k = 10 ORDER BY (SELECT MIN(distance) FROM (SELECT distance FROM region WHERE region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Areas playing a key role in economic and strategic development.') LIMIT 10) UNION SELECT MIN(distance) FROM (SELECT distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Parties advocating for social reforms and economic progress.') LIMIT 10));", + "SELECT r.Region_name FROM region r JOIN party p ON r.Region_ID = p.Region_ID WHERE r.region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Regions crucial to strategic and economic advancements.') AND r.k = 10 AND p.party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Political entities known for driving social and economic development.') AND p.k = 10 ORDER BY (SELECT MIN(distance) FROM (SELECT distance FROM region WHERE region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Regions crucial to strategic and economic advancements.') LIMIT 10) UNION SELECT MIN(distance) FROM (SELECT distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Political entities known for driving social and economic development.') LIMIT 10));", + "SELECT r.Region_name FROM region r JOIN party p ON r.Region_ID = p.Region_ID WHERE r.region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Regions integral to strategic and economic growth.') AND r.k = 10 AND p.party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Political groups focused on social and economic reforms.') AND p.k = 10 ORDER BY (SELECT MIN(distance) FROM (SELECT distance FROM region WHERE region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Regions integral to strategic and economic growth.') LIMIT 10) UNION SELECT MIN(distance) FROM (SELECT distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Political groups focused on social and economic reforms.') LIMIT 10));", + "SELECT r.Region_name FROM region r JOIN party p ON r.Region_ID = p.Region_ID WHERE r.region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Areas significant for strategic and economic contributions.') AND r.k = 10 AND p.party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Parties with a focus on social reform and economic advancement.') AND p.k = 10 ORDER BY (SELECT MIN(distance) FROM (SELECT distance FROM region WHERE region_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Areas significant for strategic and economic contributions.') LIMIT 10) UNION SELECT MIN(distance) FROM (SELECT distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Parties with a focus on social reform and economic advancement.') LIMIT 10));" + ], + "execution_status": "exception", + "error_message": "歧义错误: 在多表查询中发现无别名的向量搜索列 'region_description_embedding'。请为该列表明表别名。", + "db_type": "myscale", + "schema": "CREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Member_Name` Nullable(String),\n `Party_ID` Nullable(String),\n `In_office` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Minister` Nullable(String),\n `Took_office` Nullable(String),\n `Left_office` Nullable(String),\n `Region_ID` Nullable(Int64),\n `Party_name` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);\nCREATE TABLE party_events (\n `Event_ID` Nullable(Int64),\n `Event_Name` Nullable(String),\n `Party_ID` Nullable(Int64),\n `Member_in_charge_ID` Nullable(Int64),\n `party_events_description` Nullable(String),\n `party_events_description_embedding` Array(Float32)\n);\nCREATE TABLE region (\n `Region_ID` Nullable(Int64),\n `Region_name` Nullable(String),\n `Date` Nullable(String),\n `Label` Nullable(String),\n `Format` Nullable(String),\n `Catalogue` Nullable(String),\n `region_description` Nullable(String),\n `region_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "activity_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old student majoring in computer science from New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An outdoor recreational activity involving water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance\n FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance\n FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name\nFROM FilteredStudents fs\nJOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid)\nJOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the 5 students who are just like a 20-year-old majoring in computer science from New York, and tell me their names along with the top 5 water sports activities they do?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A young adult, 20 years old, studying computer science, residing in New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A leisure activity involving water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old computer science student living in New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Water-based recreational sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old from New York studying computer science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Water sports activities for fun') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A New York-based 20-year-old computer science major') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Outdoor water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old computer science undergraduate from New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Recreational activities involving water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Activity (\n `actid` Nullable(Int64),\n `activity_name` Nullable(String),\n `Activity_description` Nullable(String),\n `Activity_description_embedding` Array(Float32)\n);\nCREATE TABLE Activity_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Faculty_Participates_in (\n `FacID` Nullable(Int64),\n `actid` Nullable(Int64)\n);\nCREATE TABLE Faculty_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Participates_in (\n `stuid` Nullable(Int64),\n `actid` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "climbing", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'high peak in the Himalayas with difficult climb') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'experienced climber from Nepal who has won many accolades') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT \n Mountain_ID, \n Name, \n Height, \n Range, \n Country,\n distance\n FROM mountain_filtered AS mountain\n)\n\nSELECT \n c.Climber_ID AS Climber_ID\nFROM c_filtered AS c\nJOIN \n FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID)\nORDER BY \n fm.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the top 3 climbers from Nepal who have won many awards and are super experienced? They should be the ones who have climbed the top 5 high peaks in the Himalayas known for tough climbs. Could you also sort the results by how closely the mountains fit the description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'top Himalayan peaks with challenging climbs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Nepalese climbers with numerous awards and extensive experience') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n WHERE climber_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Nepalese climbers with numerous awards AND extensive experience')\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Himalayan summits known for difficult ascents') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'acclaimed climbers from Nepal with significant achievements') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'high-altitude peaks in the Himalayas requiring expert climbing skills') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'top Nepalese climbers with a history of winning awards') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'notable Himalayan peaks with strenuous climbing routes') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Nepal climbers renowned for their climbing prowess and accolades') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n WHERE climber_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Nepal climbers renowned for their climbing prowess AND accolades')\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Himalayan mountains famous for their difficult climbs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'skilled climbers from Nepal with numerous awards') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE climber (\n `Climber_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Time` Nullable(String),\n `Points` Nullable(Float64),\n `Mountain_ID` Nullable(Int64),\n `climber_description` Nullable(String),\n `climber_description_embedding` Array(Float32)\n);\nCREATE TABLE mountain (\n `Mountain_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Height` Nullable(Float64),\n `Prominence` Nullable(Float64),\n `Range` Nullable(String),\n `Country` Nullable(String),\n `mountain_description` Nullable(String),\n `mountain_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "tracking_orders", + "sql": "SELECT order_id, order_status\nFROM Orders;", + "sql_result_column_count": 2, + "sql_result_rows_count": 15, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the order IDs and their current statuses from the Orders table?", + "external_knowledge": "", + "sql_candidate": [ + "SELECT order_id, order_status\nFROM Orders;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_name` Nullable(String),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Invoices (\n `invoice_number` Nullable(Int64),\n `invoice_date` Nullable(Date),\n `invoice_details` Nullable(String),\n `Invoices_description` Nullable(String)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Nullable(Int64),\n `product_id` Int64,\n `order_id` Int64,\n `order_item_status` String,\n `order_item_details` Nullable(String)\n);\nCREATE TABLE Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status` String,\n `date_order_placed` Date,\n `order_details` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Products_description` Nullable(String)\n);\nCREATE TABLE Shipment_Items (\n `shipment_id` Int64,\n `order_item_id` Int64\n);\nCREATE TABLE Shipments (\n `shipment_id` Nullable(Int64),\n `order_id` Int64,\n `invoice_number` Int64,\n `shipment_tracking_number` Nullable(String),\n `shipment_date` Nullable(Date),\n `other_shipment_details` Nullable(String)\n);" + }, + { + "db_id": "company_office", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'under construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance\nFROM buildings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "What is the ID of the building that is most closely associated with being \"under construction\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'currently being built') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'in progress construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'ongoing construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'construction phase') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'actively under construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "entertainment_awards", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Annual festival with large audience') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance\nFROM festival_detail\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the name of a festival that's known for being a big annual event with a huge crowd?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Major annual event with a massive crowd') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Large-scale yearly festival with many attendees') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Popular annual festival attracting huge crowds') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Annual celebration known for large gatherings') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Big yearly festival with a significant audience') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artwork (\n `Artwork_ID` Nullable(Int64),\n `Type` Nullable(String),\n `Name` Nullable(String),\n `artwork_description` Nullable(String),\n `artwork_description_embedding` Array(Float32)\n);\nCREATE TABLE festival_detail (\n `Festival_ID` Nullable(Int64),\n `Festival_Name` Nullable(String),\n `Chair_Name` Nullable(String),\n `Location` Nullable(String),\n `Year` Nullable(Int64),\n `Num_of_Audience` Nullable(Int64),\n `festival_detail_description` Nullable(String),\n `festival_detail_description_embedding` Array(Float32)\n);\nCREATE TABLE nomination (\n `Artwork_ID` Nullable(Int64),\n `Festival_ID` Nullable(Int64),\n `Result` Nullable(String)\n);" + }, + { + "db_id": "company_office", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A skyscraper in a major metropolitan area with over 50 stories and modern architectural design') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance\nFROM buildings\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the five skyscrapers located in major metropolitan areas with over 50 stories and modern architectural design, and provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Skyscrapers in large cities with more than 50 floors and contemporary architecture') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tall buildings in urban centers over 50 stories high with modern design') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-rise structures in metropolitan areas featuring 50+ floors and modern architecture') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Buildings in major cities exceeding 50 stories with contemporary architectural style') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Modern skyscrapers in populous urban regions with more than 50 levels') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "company_office", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A tall skyscraper with modern architecture in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance \nFROM buildings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you identify the building that best represents a tall skyscraper with modern architecture in the city and provide its ID?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The most iconic modern skyscraper in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent high-rise with contemporary design in the urban area') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The tallest modern architectural building in the metropolis') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A leading example of modern skyscraper architecture in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An exemplary tall structure with modern design in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "train_station", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'South Eastern Main Line West of England Main Line') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Express train departing daily') AS ref_vec_1,\n\nstation_filtered AS (\n SELECT\n *,\n distance(Main_Services_embedding, ref_vec_0) AS distance\n FROM station\n\n ORDER BY distance\n LIMIT 5\n),\n\nt_filtered AS (\n SELECT\n *,\n distance(train_description_embedding, ref_vec_1) AS distance\n FROM train\n\n ORDER BY distance\n LIMIT 3\n),\n\nStationCTE AS (\n SELECT Station_ID, Name, distance AS StationDistance\n FROM station_filtered AS station\n)\n\nSELECT t.Train_ID, t.Name, s.StationDistance\nFROM t_filtered AS t\nJOIN StationCTE s ON t.Train_ID IN (\n SELECT Train_ID\n FROM train_station ts\n ORDER BY s.StationDistance\nLIMIT 2;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the two express trains that depart daily, associating them with the top 5 stations related to the South Eastern Main Line and the West of England Main Line. Make sure to include their names and distances from the main services!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'South Eastern and West of England routes') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Express services departing every day') AS ref_vec_1,\n\nstation_filtered AS (\n SELECT\n *,\n distance(Main_Services_embedding, ref_vec_0) AS distance\n FROM station\n WHERE Main_Services_embedding MATCH lembed('all-MiniLM-L6-v2', 'South Eastern AND West of England routes')\n ORDER BY distance\n LIMIT 5\n),\n\nt_filtered AS (\n SELECT\n *,\n distance(train_description_embedding, ref_vec_1) AS distance\n FROM train\n\n ORDER BY distance\n LIMIT 3\n),\n\nStationCTE AS (\n SELECT Station_ID, Name, distance AS StationDistance FROM station_filtered AS station\n)\n\nSELECT t.Train_ID, t.Name, s.StationDistance FROM t_filtered AS t JOIN StationCTE s ON t.Train_ID IN ( SELECT Train_ID FROM train_station ts ORDER BY s.StationDistance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key stations for South Eastern and West England lines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Daily express departures') AS ref_vec_1,\n\nstation_filtered AS (\n SELECT\n *,\n distance(Main_Services_embedding, ref_vec_0) AS distance\n FROM station\n WHERE Main_Services_embedding MATCH lembed('all-MiniLM-L6-v2', 'Key stations for South Eastern AND West England lines')\n ORDER BY distance\n LIMIT 5\n),\n\nt_filtered AS (\n SELECT\n *,\n distance(train_description_embedding, ref_vec_1) AS distance\n FROM train\n\n ORDER BY distance\n LIMIT 3\n),\n\nStationCTE AS (\n SELECT Station_ID, Name, distance AS StationDistance FROM station_filtered AS station\n)\n\nSELECT t.Train_ID, t.Name, s.StationDistance FROM t_filtered AS t JOIN StationCTE s ON t.Train_ID IN ( SELECT Train_ID FROM train_station ts ORDER BY s.StationDistance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Stations linked to South Eastern and West of England lines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Express trains with daily departures') AS ref_vec_1,\n\nstation_filtered AS (\n SELECT\n *,\n distance(Main_Services_embedding, ref_vec_0) AS distance\n FROM station\n WHERE Main_Services_embedding MATCH lembed('all-MiniLM-L6-v2', 'Stations linked to South Eastern AND West of England lines')\n ORDER BY distance\n LIMIT 5\n),\n\nt_filtered AS (\n SELECT\n *,\n distance(train_description_embedding, ref_vec_1) AS distance\n FROM train\n\n ORDER BY distance\n LIMIT 3\n),\n\nStationCTE AS (\n SELECT Station_ID, Name, distance AS StationDistance FROM station_filtered AS station\n)\n\nSELECT t.Train_ID, t.Name, s.StationDistance FROM t_filtered AS t JOIN StationCTE s ON t.Train_ID IN ( SELECT Train_ID FROM train_station ts ORDER BY s.StationDistance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Stations serving South Eastern and West England routes') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Daily departing express trains') AS ref_vec_1,\n\nstation_filtered AS (\n SELECT\n *,\n distance(Main_Services_embedding, ref_vec_0) AS distance\n FROM station\n WHERE Main_Services_embedding MATCH lembed('all-MiniLM-L6-v2', 'Stations serving South Eastern AND West England routes')\n ORDER BY distance\n LIMIT 5\n),\n\nt_filtered AS (\n SELECT\n *,\n distance(train_description_embedding, ref_vec_1) AS distance\n FROM train\n\n ORDER BY distance\n LIMIT 3\n),\n\nStationCTE AS (\n SELECT Station_ID, Name, distance AS StationDistance FROM station_filtered AS station\n)\n\nSELECT t.Train_ID, t.Name, s.StationDistance FROM t_filtered AS t JOIN StationCTE s ON t.Train_ID IN ( SELECT Train_ID FROM train_station ts ORDER BY s.StationDistance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Primary stations for South Eastern and West England lines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Express trains departing each day') AS ref_vec_1,\n\nstation_filtered AS (\n SELECT\n *,\n distance(Main_Services_embedding, ref_vec_0) AS distance\n FROM station\n WHERE Main_Services_embedding MATCH lembed('all-MiniLM-L6-v2', 'Primary stations for South Eastern AND West England lines')\n ORDER BY distance\n LIMIT 5\n),\n\nt_filtered AS (\n SELECT\n *,\n distance(train_description_embedding, ref_vec_1) AS distance\n FROM train\n\n ORDER BY distance\n LIMIT 3\n),\n\nStationCTE AS (\n SELECT Station_ID, Name, distance AS StationDistance FROM station_filtered AS station\n)\n\nSELECT t.Train_ID, t.Name, s.StationDistance FROM t_filtered AS t JOIN StationCTE s ON t.Train_ID IN ( SELECT Train_ID FROM train_station ts ORDER BY s.StationDistance LIMIT 2;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17445 ('(') (line 32, col 36): (\n SELECT Train_ID\n FROM train_station ts\n ORDER BY s.StationDistance\nLIMIT 2\n FORMAT Native. Unmatched parentheses: (. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE station (\n `Station_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Annual_entry_exit` Nullable(Float64),\n `Annual_interchanges` Nullable(Float64),\n `Total_Passengers` Nullable(Float64),\n `Location` Nullable(String),\n `Main_Services` Nullable(String),\n `Number_of_Platforms` Nullable(Int64),\n `station_description` Nullable(String),\n `Main_Services_embedding` Array(Float32),\n `station_description_embedding` Array(Float32)\n);\nCREATE TABLE station_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE station_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE station_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE train (\n `Train_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Time` Nullable(String),\n `Service` Nullable(String),\n `train_description` Nullable(String),\n `train_description_embedding` Array(Float32)\n);\nCREATE TABLE train_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE train_station (\n `Train_ID` Nullable(Int64),\n `Station_ID` Nullable(Int64)\n);\nCREATE TABLE train_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Rock and roll album with classic hits') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance \nFROM Album\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Which album is the closest match to a rock and roll album with classic hits?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'classic rock and roll hits album') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'album featuring classic rock and roll tracks') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'rock and roll album with timeless classics') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'album with iconic rock and roll songs') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'collection of classic rock and roll music') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "perpetrator", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'a description similar to known events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'description similar to individuals involved in incidents') AS ref_vec_1,\n\nperpetrator_filtered AS (\n SELECT\n *,\n distance(perpetrator_description_embedding, ref_vec_0) AS distance\n FROM perpetrator\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nPerpetratorSimilarity AS (\n SELECT\n Perpetrator_ID,\n People_ID,\n Date,\n Year,\n Location,\n country,\n Killed,\n Injured,\n perpetrator_description,\n distance\n FROM perpetrator_filtered AS perpetrator\n),\n\nPeopleSimilarity AS (\n SELECT\n People_ID,\n Name,\n Height,\n Weight,\n Home_Town,\n people_description,\n distance\n FROM people_filtered AS people\n)\n\nSELECT\n p.Perpetrator_ID AS Perpetrator_ID,\n pe.Name AS Name,\n p.Location AS Location,\n pe.Home_Town AS Home_Town\nFROM PerpetratorSimilarity p\nJOIN PeopleSimilarity pe ON toString(p.People_ID) = toString(pe.People_ID)\nORDER BY p.distance, pe.distance\nLIMIT 10;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you provide the names and home towns of the top 10 individuals who are most similar to those involved in described incidents and are linked to specific perpetrators based on event similarity?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'events resembling documented cases') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'individuals analogous to incident participants') AS ref_vec_1,\n\nperpetrator_filtered AS (\n SELECT\n *,\n distance(perpetrator_description_embedding, ref_vec_0) AS distance\n FROM perpetrator\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nPerpetratorSimilarity AS (\n SELECT Perpetrator_ID, People_ID, Date, Year, Location, country, Killed, Injured, perpetrator_description, distance FROM perpetrator_filtered AS perpetrator\n),\n\nPeopleSimilarity AS (\n SELECT People_ID, Name, Height, Weight, Home_Town, people_description, distance FROM people_filtered AS people\n)\n\nSELECT p.Perpetrator_ID, pe.Name, p.Location, pe.Home_Town FROM PerpetratorSimilarity p JOIN PeopleSimilarity pe ON toString(p.People_ID) = toString(pe.People_ID) ORDER BY p.distance, pe.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'events akin to historical occurrences') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'profiles similar to those in incidents') AS ref_vec_1,\n\nperpetrator_filtered AS (\n SELECT\n *,\n distance(perpetrator_description_embedding, ref_vec_0) AS distance\n FROM perpetrator\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nPerpetratorSimilarity AS (\n SELECT Perpetrator_ID, People_ID, Date, Year, Location, country, Killed, Injured, perpetrator_description, distance FROM perpetrator_filtered AS perpetrator\n),\n\nPeopleSimilarity AS (\n SELECT People_ID, Name, Height, Weight, Home_Town, people_description, distance FROM people_filtered AS people\n)\n\nSELECT p.Perpetrator_ID, pe.Name, p.Location, pe.Home_Town FROM PerpetratorSimilarity p JOIN PeopleSimilarity pe ON toString(p.People_ID) = toString(pe.People_ID) ORDER BY p.distance, pe.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'descriptions matching prior events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'people resembling those in past incidents') AS ref_vec_1,\n\nperpetrator_filtered AS (\n SELECT\n *,\n distance(perpetrator_description_embedding, ref_vec_0) AS distance\n FROM perpetrator\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nPerpetratorSimilarity AS (\n SELECT Perpetrator_ID, People_ID, Date, Year, Location, country, Killed, Injured, perpetrator_description, distance FROM perpetrator_filtered AS perpetrator\n),\n\nPeopleSimilarity AS (\n SELECT People_ID, Name, Height, Weight, Home_Town, people_description, distance FROM people_filtered AS people\n)\n\nSELECT p.Perpetrator_ID, pe.Name, p.Location, pe.Home_Town FROM PerpetratorSimilarity p JOIN PeopleSimilarity pe ON toString(p.People_ID) = toString(pe.People_ID) ORDER BY p.distance, pe.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'cases reflecting known events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'descriptions akin to those involved in incidents') AS ref_vec_1,\n\nperpetrator_filtered AS (\n SELECT\n *,\n distance(perpetrator_description_embedding, ref_vec_0) AS distance\n FROM perpetrator\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nPerpetratorSimilarity AS (\n SELECT Perpetrator_ID, People_ID, Date, Year, Location, country, Killed, Injured, perpetrator_description, distance FROM perpetrator_filtered AS perpetrator\n),\n\nPeopleSimilarity AS (\n SELECT People_ID, Name, Height, Weight, Home_Town, people_description, distance FROM people_filtered AS people\n)\n\nSELECT p.Perpetrator_ID, pe.Name, p.Location, pe.Home_Town FROM PerpetratorSimilarity p JOIN PeopleSimilarity pe ON toString(p.People_ID) = toString(pe.People_ID) ORDER BY p.distance, pe.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'events comparable to known cases') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'individuals comparable to those in incidents') AS ref_vec_1,\n\nperpetrator_filtered AS (\n SELECT\n *,\n distance(perpetrator_description_embedding, ref_vec_0) AS distance\n FROM perpetrator\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nPerpetratorSimilarity AS (\n SELECT Perpetrator_ID, People_ID, Date, Year, Location, country, Killed, Injured, perpetrator_description, distance FROM perpetrator_filtered AS perpetrator\n),\n\nPeopleSimilarity AS (\n SELECT People_ID, Name, Height, Weight, Home_Town, people_description, distance FROM people_filtered AS people\n)\n\nSELECT p.Perpetrator_ID, pe.Name, p.Location, pe.Home_Town FROM PerpetratorSimilarity p JOIN PeopleSimilarity pe ON toString(p.People_ID) = toString(pe.People_ID) ORDER BY p.distance, pe.distance LIMIT 10;" + ], + "integration_level": 7, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'country' while processing query: 'WITH [0.02901238016784191, 0.06251802295446396, 0.015513896942138672, 0.039820361882448196, 0.0446757972240448, 0.027723398059606552, 0.058569591492414474, -0.01626882329583168, 0.05737563595175743, 0.02384852059185505, 0.0029692170210182667, -0.009868458844721317, 0.009954771026968956, 0.07439526915550232, -0.05068500339984894, 0.0020964648574590683, 0.02889292687177658, 0.007999006658792496, -0.1267705261707306, -0.015735484659671783, -0.004294287413358688, -0.07474537938833237, -0.06353650987148285, 0.05742555111646652, -0.04406222701072693, 0.02766098640859127, 0.042015839368104935, -0.006062384694814682, -0.007821841165423393, -0.014796259813010693, 0.013887942768633366, -0.057628706097602844, 0.053433407098054886, 0.01080892514437437, 0.03341048210859299, 0.022035982459783554, -0.03828105330467224, 0.040096744894981384, -0.0010358052095398307, -0.045736342668533325, 0.0218758974224329, -0.05211212858557701, 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0.024415001273155212, 0.05849499627947807, -0.03892903029918671, 0.04712354391813278, 0.026347290724515915, -0.011016537435352802, 0.04572214558720589, 0.09215687215328217, 0.07425767183303833, -0.043951116502285004, 0.02254589833319187, 0.010212434455752373, 0.0036694700829684734, -0.08979760110378265, 0.022487815469503403, -0.06873295456171036, 0.050296369940042496, -0.027408966794610023, 0.048453327268362045, 0.008644436486065388, -0.029109926894307137, 0.002546584000810981, 0.04692209139466286, 0.037374719977378845, -0.0523567758500576, 0.06546017527580261, -0.07910206913948059, -0.008687537163496017, -0.021948600187897682, 0.06787344068288803, 0.11350221931934357, 0.18951664865016937, 0.02896338887512684, -0.0745086818933487, -0.034708231687545776, -0.04585966840386391, 0.00631348043680191, -0.014768915250897408, -0.00414083618670702, -0.03592103347182274, 0.0995800793170929, -0.0011939344694837928, 0.03686325252056122, 0.07806282490491867, 0.025358835235238075, 0.03626340627670288, -0.07063216716051102, 0.07420261204242706, 0.08048024028539658, 0.014209556393325329, -0.03422890976071358] AS ref_vec_1 SELECT Perpetrator_ID, People_ID, Date, Year, Location, country, Killed, Injured, perpetrator_description, distance FROM perpetrator_filtered AS perpetrator', required columns: 'Perpetrator_ID' 'Killed' 'People_ID' 'Date' 'Year' 'country' 'Injured' 'perpetrator_description' 'Location' 'distance' 'Perpetrator_ID' 'Killed' 'People_ID' 'Date' 'Year' 'country' 'Injured' 'perpetrator_description' 'Location' 'distance'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE people (\n `People_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Height` Nullable(Float64),\n `Weight` Nullable(Float64),\n `Home_Town` Nullable(String),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);\nCREATE TABLE perpetrator (\n `Perpetrator_ID` Nullable(Int64),\n `People_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Year` Nullable(Float64),\n `Location` Nullable(String),\n `Country` Nullable(String),\n `Killed` Nullable(Int64),\n `Injured` Nullable(Int64),\n `perpetrator_description` Nullable(String),\n `perpetrator_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "journal_committee", + "sql": "WITH RankedJournals AS (\n SELECT \n j.Journal_ID AS Journal_ID, \n j.Sales AS Sales, \n RANK() OVER (ORDER BY j.Sales DESC) AS SalesRank\n FROM \n journal j\n),\nTopEditors AS (\n SELECT \n jc.Editor_ID AS Editor_ID,\n e.Name AS Name,\n COUNT(DISTINCT rj.Journal_ID) AS NumberOfTopJournals\n FROM \n RankedJournals rj\n JOIN \n journal_committee jc ON toString(rj.Journal_ID) = toString(jc.Journal_ID)\n JOIN \n editor e ON toString(jc.Editor_ID) = toString(e.Editor_ID)\n WHERE \n rj.SalesRank <= 5 \n GROUP BY \n jc.Editor_ID, e.Name\n ORDER BY \n NumberOfTopJournals DESC\n)\nSELECT \n te.Name AS Name\nFROM \n TopEditors te\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the editor who is associated with the highest number of top 5 best-selling journals and provide me with their name?", + "external_knowledge": "", + "sql_candidate": [ + "WITH RankedJournals AS (\n SELECT \n j.Journal_ID AS Journal_ID, \n j.Sales AS Sales, \n RANK() OVER (ORDER BY j.Sales DESC) AS SalesRank\n FROM \n journal j\n),\nTopEditors AS (\n SELECT \n jc.Editor_ID AS Editor_ID,\n e.Name AS Name,\n COUNT(DISTINCT rj.Journal_ID) AS NumberOfTopJournals\n FROM \n RankedJournals rj\n JOIN \n journal_committee jc ON toString(rj.Journal_ID) = toString(jc.Journal_ID)\n JOIN \n editor e ON toString(jc.Editor_ID) = toString(e.Editor_ID)\n WHERE \n rj.SalesRank <= 5 \n GROUP BY \n jc.Editor_ID, e.Name\n ORDER BY \n NumberOfTopJournals DESC\n)\nSELECT \n te.Name AS Name\nFROM \n TopEditors te\nLIMIT 1;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE editor (\n `Editor_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Float64),\n `editor_description` Nullable(String)\n);\nCREATE TABLE journal (\n `Journal_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Theme` Nullable(String),\n `Sales` Nullable(Int64),\n `journal_description` Nullable(String)\n);\nCREATE TABLE journal_committee (\n `Editor_ID` Nullable(Int64),\n `Journal_ID` Nullable(Int64),\n `Work_Type` Nullable(String)\n);" + }, + { + "db_id": "insurance_fnol", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Customer referred to as America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance\nFROM Customers c\nJOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the Customer_ID of the customer most closely matching the description of \"America Jaskolski with ID 194\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Customer identified as America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Find customer called America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Locate customer known as America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for customer America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Customer named America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Available_Policies (\n `Policy_ID` Nullable(Int64),\n `policy_type_code` Nullable(String),\n `Customer_Phone` Nullable(String),\n `Available_Policies_description` Nullable(String),\n `Available_Policies_description_embedding` Array(Float32)\n);\nCREATE TABLE Available_Policies_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Claims (\n `Claim_ID` Int64,\n `FNOL_ID` Int64,\n `Effective_Date` Nullable(Date)\n);\nCREATE TABLE Customers (\n `Customer_ID` Nullable(Int64),\n `Customer_name` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers_Policies (\n `Customer_ID` Int64,\n `Policy_ID` Int64,\n `Date_Opened` Nullable(Date),\n `Date_Closed` Nullable(Date)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE First_Notification_of_Loss (\n `FNOL_ID` Int64,\n `Customer_ID` Int64,\n `Policy_ID` Int64,\n `Service_ID` Int64\n);\nCREATE TABLE Services (\n `Service_ID` Nullable(Int64),\n `Service_name` Nullable(String),\n `Services_description` Nullable(String),\n `Services_description_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Settlements (\n `Settlement_ID` Int64,\n `Claim_ID` Nullable(Int64),\n `Effective_Date` Nullable(Date),\n `Settlement_Amount` Nullable(Float64)\n);" + }, + { + "db_id": "scholar", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Deep Learning') AS ref_vec_0,\n\nsimilar_papers AS (\n SELECT p.paperId, p.title, p.numCitedBy, p.numCiting, distance(p.title_embedding, ref_vec_0) AS distance\n FROM paper p\n ORDER BY distance\n LIMIT 5\n),\n\ncitation_analysis AS (\n SELECT sp.paperId, \n sp.title AS title,\n sp.numCitedBy AS numCitedBy,\n (SELECT COUNT(*) FROM cite c WHERE c.citingPaperId = sp.paperId) AS numTimesCited\n FROM similar_papers sp\n)\n\nSELECT ca.paperId, ca.title, ca.numTimesCited\nFROM citation_analysis ca\nORDER BY ca.numTimesCited DESC, ca.numCitedBy DESC\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the top 10 papers related to Deep Learning? I'm interested to know which ones are cited the most by others!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep Learning research') AS ref_vec_0,\n\nsimilar_papers AS (\n SELECT p.paperId, p.title, p.numCitedBy, p.numCiting, distance(p.title_embedding, ref_vec_0) AS distance FROM paper p\n ORDER BY distance\n LIMIT 5\n),\n\ncitation_analysis AS (\n SELECT sp.paperId, sp.title, sp.numCitedBy, (SELECT COUNT(*) FROM cite c WHERE c.citingPaperId = sp.paperId) AS numTimesCited FROM similar_papers sp\n)\n\nSELECT ca.paperId, ca.title, ca.numTimesCited FROM citation_analysis ca ORDER BY ca.numTimesCited DESC, ca.numCitedBy DESC LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep Learning publications') AS ref_vec_0,\n\nsimilar_papers AS (\n SELECT p.paperId, p.title, p.numCitedBy, p.numCiting, distance(p.title_embedding, ref_vec_0) AS distance FROM paper p\n ORDER BY distance\n LIMIT 5\n),\n\ncitation_analysis AS (\n SELECT sp.paperId, sp.title, sp.numCitedBy, (SELECT COUNT(*) FROM cite c WHERE c.citingPaperId = sp.paperId) AS numTimesCited FROM similar_papers sp\n)\n\nSELECT ca.paperId, ca.title, ca.numTimesCited FROM citation_analysis ca ORDER BY ca.numTimesCited DESC, ca.numCitedBy DESC LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep Learning articles') AS ref_vec_0,\n\nsimilar_papers AS (\n SELECT p.paperId, p.title, p.numCitedBy, p.numCiting, distance(p.title_embedding, ref_vec_0) AS distance FROM paper p\n ORDER BY distance\n LIMIT 5\n),\n\ncitation_analysis AS (\n SELECT sp.paperId, sp.title, sp.numCitedBy, (SELECT COUNT(*) FROM cite c WHERE c.citingPaperId = sp.paperId) AS numTimesCited FROM similar_papers sp\n)\n\nSELECT ca.paperId, ca.title, ca.numTimesCited FROM citation_analysis ca ORDER BY ca.numTimesCited DESC, ca.numCitedBy DESC LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep Learning studies') AS ref_vec_0,\n\nsimilar_papers AS (\n SELECT p.paperId, p.title, p.numCitedBy, p.numCiting, distance(p.title_embedding, ref_vec_0) AS distance FROM paper p\n ORDER BY distance\n LIMIT 5\n),\n\ncitation_analysis AS (\n SELECT sp.paperId, sp.title, sp.numCitedBy, (SELECT COUNT(*) FROM cite c WHERE c.citingPaperId = sp.paperId) AS numTimesCited FROM similar_papers sp\n)\n\nSELECT ca.paperId, ca.title, ca.numTimesCited FROM citation_analysis ca ORDER BY ca.numTimesCited DESC, ca.numCitedBy DESC LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Deep Learning works') AS ref_vec_0,\n\nsimilar_papers AS (\n SELECT p.paperId, p.title, p.numCitedBy, p.numCiting, distance(p.title_embedding, ref_vec_0) AS distance FROM paper p\n ORDER BY distance\n LIMIT 5\n),\n\ncitation_analysis AS (\n SELECT sp.paperId, sp.title, sp.numCitedBy, (SELECT COUNT(*) FROM cite c WHERE c.citingPaperId = sp.paperId) AS numTimesCited FROM similar_papers sp\n)\n\nSELECT ca.paperId, ca.title, ca.numTimesCited FROM citation_analysis ca ORDER BY ca.numTimesCited DESC, ca.numCitedBy DESC LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'sp.paperId' while processing query: 'WITH [-0.1002454087138176, -0.049835801124572754, 0.06778673827648163, 0.004215118940919638, -0.047210466116666794, 0.01713956706225872, -0.002271898090839386, -0.017115896567702293, -0.02959003672003746, -0.11871316283941269, -0.038166020065546036, 0.020351624116301537, -0.014855258166790009, 0.034485116600990295, -0.11386054754257202, 0.02793453261256218, 0.04010980576276779, 0.04308731108903885, -0.18421700596809387, -0.06481398642063141, -0.02552417665719986, -0.0021843109279870987, -0.030128445476293564, -0.013696115463972092, 0.060191135853528976, 0.04344550892710686, 0.03237748518586159, -0.011177719570696354, 0.05878124013543129, -0.11659087985754013, 0.08325430750846863, -0.03398314490914345, 0.027385205030441284, 0.05018647387623787, -0.067144013941288, 0.04789123311638832, -0.07389369606971741, 0.023334702476859093, 0.034407079219818115, -0.0014689689269289374, -0.028689149767160416, -0.038504816591739655, 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-0.009406781755387783, 0.07241234928369522, 0.010023662820458412, 0.05638669803738594, 0.05783451721072197, -0.00911096390336752, -0.061272185295820236, 0.05870947614312172, -0.027939772233366966, 0.04874343425035477, 0.031075550243258476, 0.015385756269097328, 0.009477726183831692, -0.011686692014336586, 0.11741136014461517, 0.11699620634317398, 0.0372309572994709, 0.014605045318603516, 0.07149559259414673, -0.0046302806586027145, 0.024778662249445915, 0.08472704142332077, 0.00481570465490222, 0.01481341477483511, -0.036445099860429764, 0.10725386440753937, 0.011379276402294636, -0.024361975491046906, 0.0681024119257927, 0.10204996913671494, 0.0326266810297966, 0.011572739109396935, 0.003360274014994502, 0.014055863954126835, -0.017765384167432785, 0.0852111428976059, -0.06707054376602173, -0.046018924564123154, -0.027919981628656387, -0.01382761262357235, -0.007406953722238541, -0.07859039306640625, 0.048083048313856125, -0.017803490161895752, -0.0016820812597870827, 0.0573815256357193, 0.0680990219116211, 0.08413161337375641, 0.030487384647130966, 0.041726671159267426, 0.029013952240347862, -0.04777635633945465, -0.03083805926144123, 0.0854356437921524, -0.026016848161816597, 0.061621613800525665, -0.061395905911922455, -0.022018559277057648] AS ref_vec_0 SELECT count(*) FROM cite AS c WHERE c.citingPaperId = sp.paperId) AS numTimesCited. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE author (\n `authorId` Int64,\n `authorName` Nullable(String),\n `author_description` Nullable(String)\n);\nCREATE TABLE cite (\n `citingPaperId` Int64,\n `citedPaperId` Int64\n);\nCREATE TABLE dataset (\n `datasetId` Int64,\n `datasetName` Nullable(String),\n `dataset_description` Nullable(String)\n);\nCREATE TABLE journal (\n `journalId` Int64,\n `journalName` Nullable(String),\n `journal_description` Nullable(String)\n);\nCREATE TABLE keyphrase (\n `keyphraseId` Int64,\n `keyphraseName` Nullable(String)\n);\nCREATE TABLE paper (\n `paperId` Nullable(Int64),\n `title` Nullable(String),\n `venueId` Nullable(Int64),\n `year` Nullable(Int64),\n `numCiting` Nullable(Int64),\n `numCitedBy` Nullable(Int64),\n `journalId` Nullable(Int64),\n `paper_description` Nullable(String),\n `title_embedding` Array(Float32)\n);\nCREATE TABLE paperDataset (\n `paperId` Nullable(Int64),\n `datasetId` Nullable(Int64)\n);\nCREATE TABLE paperKeyphrase (\n `paperId` Nullable(Int64),\n `keyphraseId` Nullable(Int64)\n);\nCREATE TABLE paper_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE paper_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE venue (\n `venueId` Int64,\n `venueName` Nullable(String),\n `venue_description` Nullable(String)\n);\nCREATE TABLE writes (\n `paperId` Nullable(Int64),\n `authorId` Nullable(Int64)\n);" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renewable energy project focused on solar and wind power') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance \nFROM Projects\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you find me the top project that's all about renewable energy with a focus on solar and wind power? I just need the project ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A project on renewable energy with emphasis on solar and wind') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top renewable energy project focusing on solar and wind') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading project in renewable energy, specializing in solar and wind power') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renewable energy initiative centered around solar and wind energy') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Major renewable energy project with solar and wind focus') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of renewable energy initiatives') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance\nFROM Projects\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the project that best matches the exploration of renewable energy initiatives and share its ID with me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigation into renewable energy projects') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on sustainable energy solutions') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of renewable energy initiatives') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of green energy projects') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of sustainable energy initiatives') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "customers_and_products_contacts", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Product description similar to a high-end gadget') AS ref_vec_0\n\nSELECT c.customer_name, p.product_name, distance(p.Products_description_embedding, ref_vec_0) AS distance\nFROM Products p\nJOIN Order_Items oi ON toString(p.product_id) = toString(oi.product_id)\nJOIN Customer_Orders co ON toString(oi.order_id) = toString(co.order_id)\nJOIN Customers c ON toString(co.customer_id) = toString(c.customer_id)\nWHERE co.order_status_code = 'Completed'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the names of customers who ordered the top 5 products that are like a high-end gadget? Make sure their orders are completed, and I'd love to know the product names and how closely they match that gadgety vibe.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'High-end electronic gadget-like products') AS ref_vec_0\n\nSELECT c.customer_name, p.product_name, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Order_Items oi ON toString(p.product_id) = toString(oi.product_id) JOIN Customer_Orders co ON toString(oi.order_id) = toString(co.order_id) JOIN Customers c ON toString(co.customer_id) = toString(c.customer_id) WHERE co.order_status_code = 'Completed'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-tier gadget-inspired items') AS ref_vec_0\n\nSELECT c.customer_name, p.product_name, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Order_Items oi ON toString(p.product_id) = toString(oi.product_id) JOIN Customer_Orders co ON toString(oi.order_id) = toString(co.order_id) JOIN Customers c ON toString(co.customer_id) = toString(c.customer_id) WHERE co.order_status_code = 'Completed'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Luxury gadget-like product description') AS ref_vec_0\n\nSELECT c.customer_name, p.product_name, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Order_Items oi ON toString(p.product_id) = toString(oi.product_id) JOIN Customer_Orders co ON toString(oi.order_id) = toString(co.order_id) JOIN Customers c ON toString(co.customer_id) = toString(c.customer_id) WHERE co.order_status_code = 'Completed'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Premium gadget-esque products') AS ref_vec_0\n\nSELECT c.customer_name, p.product_name, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Order_Items oi ON toString(p.product_id) = toString(oi.product_id) JOIN Customer_Orders co ON toString(oi.order_id) = toString(co.order_id) JOIN Customers c ON toString(co.customer_id) = toString(c.customer_id) WHERE co.order_status_code = 'Completed'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sophisticated gadget-inspired product descriptions') AS ref_vec_0\n\nSELECT c.customer_name, p.product_name, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Order_Items oi ON toString(p.product_id) = toString(oi.product_id) JOIN Customer_Orders co ON toString(oi.order_id) = toString(co.order_id) JOIN Customers c ON toString(co.customer_id) = toString(c.customer_id) WHERE co.order_status_code = 'Completed'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'Products_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Contacts (\n `contact_id` Nullable(Int64),\n `customer_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `contact_phone` Nullable(String),\n `Contacts_description` Nullable(String),\n `Contacts_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Address_History (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customer_Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_date` Date,\n `order_status_code` Nullable(String)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `customer_number` Nullable(String),\n `customer_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Int64,\n `order_id` Int64,\n `product_id` Int64,\n `order_quantity` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_type_code` Nullable(String),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "store_product", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'High-resolution scanner with USB connectivity') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance\nFROM product\nORDER BY distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the IDs and names of the 2 products that best fit the description of a \"High-resolution scanner with USB connectivity\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'USB-enabled high-resolution scanner') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-res scanner with USB port') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Scanner with USB connection and high resolution') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-definition scanner featuring USB connectivity') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced scanner with USB interface and high resolution') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE district (\n `District_ID` Nullable(Int64),\n `District_name` Nullable(String),\n `Headquartered_City` Nullable(String),\n `City_Population` Nullable(Float64),\n `City_Area` Nullable(Float64),\n `district_description` Nullable(String),\n `district_description_embedding` Array(Float32)\n);\nCREATE TABLE product (\n `product_id` Nullable(Int64),\n `product` Nullable(String),\n `dimensions` Nullable(String),\n `dpi` Nullable(Float64),\n `pages_per_minute_color` Nullable(Float64),\n `max_page_size` Nullable(String),\n `interface` Nullable(String),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE store (\n `Store_ID` Nullable(Int64),\n `Store_Name` Nullable(String),\n `Type` Nullable(String),\n `Area_size` Nullable(Float64),\n `Number_of_product_category` Nullable(Float64),\n `Ranking` Nullable(Int64),\n `store_description` Nullable(String),\n `store_description_embedding` Array(Float32)\n);\nCREATE TABLE store_district (\n `Store_ID` Nullable(Int64),\n `District_ID` Nullable(Int64)\n);\nCREATE TABLE store_product (\n `Store_ID` Nullable(Int64),\n `Product_ID` Nullable(Int64)\n);" + }, + { + "db_id": "soccer_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'player with excellent skills and no yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Tryout t ON toString(p.pID) = toString(t.pID)\nWHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the names of the top 5 players who are highly skilled and have no yellow cards and were accepted in a tryout?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'highly skilled player without yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top player with no yellow cards and great skills') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'elite player, skillful and no yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'player with high skill level and zero yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'exceptionally skilled player without any yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE College (\n `cName` Nullable(String),\n `state` Nullable(String),\n `enr` Nullable(Float64),\n `College_description` Nullable(String),\n `College_description_embedding` Array(Float32)\n);\nCREATE TABLE College_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Player (\n `pID` Nullable(Float64),\n `pName` Nullable(String),\n `yCard` Nullable(String),\n `HS` Nullable(Float64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Tryout (\n `pID` Nullable(Decimal(38, 6)),\n `cName` Nullable(String),\n `pPos` Nullable(String),\n `decision` Nullable(String)\n);" + }, + { + "db_id": "local_govt_mdm", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A company specializing in scientific journals and books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance\nFROM Customer_Master_Index cmi\nJOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 9, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Can you help me out by finding the top 5 customer IDs and their descriptions for companies that specialize in scientific journals and books? I'd also love to know how close each match is. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Organizations focused on publishing scientific literature and academic books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Businesses that produce scientific journals and educational books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Enterprises involved in the distribution of scientific publications and textbooks.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Companies that specialize in the publication of scientific and academic resources.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Firms dedicated to the creation of scientific articles and scholarly books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Benefits_Overpayments (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Business_Rates (\n `business_rates_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE CMI_Cross_References (\n `cmi_cross_ref_id` Int64,\n `master_customer_id` Int64,\n `source_system_code` String\n);\nCREATE TABLE Council_Tax (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Customer_Master_Index (\n `master_customer_id` Nullable(Int64),\n `cmi_details` Nullable(String),\n `Customer_Master_Index_description` Nullable(String),\n `cmi_details_embedding` Array(Float32),\n `Customer_Master_Index_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Electoral_Register (\n `electoral_register_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Parking_Fines (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Rent_Arrears (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);" + }, + { + "db_id": "mountain_photos", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A towering peak in the Himalayas known for its rugged terrain and breathtaking views.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance\nFROM mountain\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Identify the mountain that best matches the description of a towering Himalayan peak with rugged terrain and breathtaking views. Provide its ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A majestic Himalayan mountain with steep slopes and stunning vistas.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent peak in the Himalayas featuring rugged landscapes and scenic views.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A towering Himalayan summit known for its challenging terrain and spectacular scenery.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A high-altitude Himalayan mountain with rough terrain and breathtaking panoramas.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A striking Himalayan peak characterized by its rugged environment and awe-inspiring views.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE camera_lens (\n `id` Nullable(Int64),\n `brand` Nullable(String),\n `name` Nullable(String),\n `focal_length_mm` Nullable(Float64),\n `max_aperture` Nullable(Float64),\n `camera_lens_description` Nullable(String),\n `camera_lens_description_embedding` Array(Float32)\n);\nCREATE TABLE mountain (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Height` Nullable(Float64),\n `Prominence` Nullable(Float64),\n `Range` Nullable(String),\n `Country` Nullable(String),\n `mountain_description` Nullable(String),\n `mountain_description_embedding` Array(Float32)\n);\nCREATE TABLE photos (\n `id` Nullable(Int64),\n `camera_lens_id` Nullable(Int64),\n `mountain_id` Nullable(Int64),\n `color` Nullable(String),\n `name` Nullable(String),\n `photos_description` Nullable(String),\n `photos_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "tvshow", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling episode with unexpected plot twists and high ratings') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance\n FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name\nFROM TV_Channel t\nJOIN SeriesKNN s ON toString(t.series_name) = toString(s.id)\nORDER BY s.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "What are the names of the top five TV series that align with a thrilling narrative filled with unexpected plot twists and high ratings?", + "external_knowledge": "In vector search operations, the \"MATCH\" operator is used for approximate nearest neighbor search to find vectors that are closest in terms of Euclidean distance. The \"k=5\" in the query specifies that the top 5 items are to be retrieved. In this context, embeddings are numerical representations of text data that capture semantic meaning. The series descriptions are compared to the embedding of a specified phrase to find the most semantically similar entries, with the smallest distance indicating the highest similarity.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exhilarating series with surprising plot developments and high viewer ratings') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A suspenseful show with unpredictable storylines and excellent ratings') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-rated thrilling series with unexpected twists and turns') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-rated TV series with thrilling narratives and surprise plot changes') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A captivating series with high ratings and unexpected plot twists') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Cartoon (\n `id` Nullable(Float64),\n `Title` Nullable(String),\n `Directed_by` Nullable(String),\n `Written_by` Nullable(String),\n `Original_air_date` Nullable(String),\n `Production_code` Nullable(Float64),\n `Channel` Nullable(String),\n `Cartoon_description` Nullable(String),\n `Cartoon_description_embedding` Array(Float32)\n);\nCREATE TABLE TV_Channel (\n `id` Nullable(String),\n `series_name` Nullable(String),\n `Country` Nullable(String),\n `Language` Nullable(String),\n `Content` Nullable(String),\n `Pixel_aspect_ratio_PAR` Nullable(String),\n `Hight_definition_TV` Nullable(String),\n `Pay_per_view_PPV` Nullable(String),\n `Package_Option` Nullable(String),\n `TV_Channel_description` Nullable(String),\n `TV_Channel_description_embedding` Array(Float32)\n);\nCREATE TABLE TV_series (\n `id` Nullable(Float64),\n `Episode` Nullable(String),\n `Air_Date` Nullable(String),\n `Rating` Nullable(String),\n `Share` Nullable(Float64),\n `fld_18_49_Rating_Share` Nullable(String),\n `Viewers_m` Nullable(String),\n `Weekly_Rank` Nullable(Float64),\n `Channel` Nullable(String),\n `TV_series_description` Nullable(String),\n `TV_series_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "local_govt_and_lot", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Satisfied services for residents') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance\nFROM Residents_Services rs\nJOIN Properties p ON toString(rs.property_id) = toString(p.property_id)\nWHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top 5 services provided to residents that are considered satisfactory, specifically for properties located in Springfield.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top satisfactory services for Springfield residents') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Services rated satisfactory by Springfield residents') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Springfield properties with satisfactory services') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly rated services for Springfield properties') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Springfield resident services deemed satisfactory') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Customer_Event_Notes (\n `Customer_Event_Note_ID` Int64,\n `Customer_Event_ID` Int64,\n `service_type_code` String,\n `resident_id` Int64,\n `property_id` Int64,\n `date_moved_in` Date,\n `Customer_Event_Notes_description` Nullable(String)\n);\nCREATE TABLE Customer_Events (\n `Customer_Event_ID` Int64,\n `customer_id` Nullable(Int64),\n `date_moved_in` Nullable(Date),\n `property_id` Nullable(Int64),\n `resident_id` Nullable(Int64),\n `thing_id` Int64\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String),\n `customer_details_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `parent_organization_id` Nullable(Int64),\n `organization_details` Nullable(String),\n `Organizations_description` Nullable(String),\n `organization_details_embedding` Array(Float32)\n);\nCREATE TABLE Organizations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` Nullable(String),\n `property_address` Nullable(String),\n `other_details` Nullable(String),\n `Properties_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Properties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents (\n `resident_id` Nullable(Int64),\n `property_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `date_moved_out` Nullable(String),\n `other_details` Nullable(String),\n `Residents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services (\n `resident_id` Nullable(Int64),\n `service_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `property_id` Nullable(Int64),\n `date_requested` Nullable(String),\n `date_provided` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Services_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Things (\n `thing_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `Type_of_Thing_Code` Nullable(String),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Things_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Things_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Timed_Locations_of_Things (\n `thing_id` Int64,\n `Date_and_Time` Date,\n `Location_Code` String\n);\nCREATE TABLE Timed_Status_of_Things (\n `thing_id` Int64,\n `Date_and_Date` Date,\n `Status_of_Thing_Code` String\n);" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A classic rock album with iconic hits') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance\nFROM Album\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need to identify the album that best represents a classic rock album with iconic hits. Could you provide me with the AlbumId for this?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic collection of classic rock hits') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The quintessential classic rock album with famous tracks') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary album featuring classic rock anthems') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A definitive classic rock album known for its hit songs') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A celebrated rock album with classic chart-toppers') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high-energy rock track with powerful guitar riffs and dynamic vocals.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance\nFROM Track\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Identify the top 3 tracks with high-energy rock themes, characterized by powerful guitar riffs and dynamic vocals.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Energetic rock music featuring strong guitar riffs and dynamic singing.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rock tracks with intense guitar solos and powerful vocal performances.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-energy rock songs with prominent guitar and vibrant vocals.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dynamic rock tracks characterized by bold guitar riffs and energetic vocals.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rock music with powerful guitar riffs and lively vocal dynamics.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "pets_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John, a 20-year-old male majoring in Computer Science, advised by 1234, from New York.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance \nFROM Student\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Please find the student ID and similarity score for the student whose profile most closely matches the description: \"John, a 20-year-old male majoring in Computer Science, advised by 1234, from New York.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the student ID and similarity score for a male student, 20 years old, studying Computer Science, advisor ID 1234, from New York.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '20-year-old Computer Science major, male, advised by 1234, located in New York, find student ID and score.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify a student ID and score for a 20-year-old male in Computer Science with advisor 1234, from New York.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for a student, 20-year-old male in Computer Science, advised by 1234, based in New York, and return ID and similarity.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Locate the student ID and similarity for a New York-based, 20-year-old male studying Computer Science, advised by 1234.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Has_Pet (\n `StuID` Nullable(Int64),\n `PetID` Nullable(Int64)\n);\nCREATE TABLE Pets (\n `PetID` Nullable(Int64),\n `PetType` Nullable(String),\n `pet_age` Nullable(Int64),\n `weight` Nullable(Float64),\n `Pets_description` Nullable(String),\n `Pets_description_embedding` Array(Float32)\n);\nCREATE TABLE Pets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Research funding document for innovative projects') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance\nFROM Documents d\nJOIN Grants g ON toString(d.grant_id) = toString(g.grant_id)\nWHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please identify the three most significant documents related to \"Research funding document for innovative projects\" that are associated with grants exceeding $50,000? I need their document IDs and type codes!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Documents on funding for innovative research projects') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative project research funding documents') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Funding documents for research on innovation') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research funding documents for innovative projects') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Documents related to funding for innovative research') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Important document regarding funding over $50,000') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount\n FROM Grants\n WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance\nFROM Documents d\nJOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "What are the document IDs for the top 5 documents associated with grants over $50,000, that are most relevant to important funding documents?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top funding documents related to grants exceeding $50,000') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Significant funding documentation for large grants') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key documents on substantial funding grants') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Relevant documents for major funding grants over $50,000') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Important documents linked to high-value grants') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "local_govt_and_lot", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'recent medical check-up') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT \n rs.resident_id AS resident_id, \n rs.service_id AS service_id, \n rs.date_requested AS date_requested,\n s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance\n FROM \n Residents_Services rs\n JOIN \n Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT \n r.resident_id AS resident_id, \n r.property_id AS property_id, \n o.organization_id AS organization_id\n FROM \n Residents r\n JOIN \n Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT \n ro.resident_id AS resident_id\nFROM \n Resident_Organization ro\nJOIN \n Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id)\nWHERE \n ro.organization_id = rs.organization_id\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who is one resident recently connected to an organization through a medical service request that aligns with the latest types of medical check-ups?", + "external_knowledge": "The `MATCH` operator used in the query performs an approximate nearest neighbor (ANN) search, which is a method to find entities that are semantically similar to a given concept. Here, the concept is \"recent medical check-up\", and the search retrieves the top 5 most similar service requests using vector embeddings. The embeddings translate textual content into numeric vectors where similarity is gauged by Euclidean distance (L2 norm). The closer the distance, the higher the similarity, allowing the system to infer relatedness beyond exact keyword matches.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'latest medical service request') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'newest types of health check-ups') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'current medical examinations') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'recent healthcare service requests') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'latest medical assessment') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Customer_Event_Notes (\n `Customer_Event_Note_ID` Int64,\n `Customer_Event_ID` Int64,\n `service_type_code` String,\n `resident_id` Int64,\n `property_id` Int64,\n `date_moved_in` Date,\n `Customer_Event_Notes_description` Nullable(String)\n);\nCREATE TABLE Customer_Events (\n `Customer_Event_ID` Int64,\n `customer_id` Nullable(Int64),\n `date_moved_in` Nullable(Date),\n `property_id` Nullable(Int64),\n `resident_id` Nullable(Int64),\n `thing_id` Int64\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String),\n `customer_details_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `parent_organization_id` Nullable(Int64),\n `organization_details` Nullable(String),\n `Organizations_description` Nullable(String),\n `organization_details_embedding` Array(Float32)\n);\nCREATE TABLE Organizations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` Nullable(String),\n `property_address` Nullable(String),\n `other_details` Nullable(String),\n `Properties_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Properties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents (\n `resident_id` Nullable(Int64),\n `property_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `date_moved_out` Nullable(String),\n `other_details` Nullable(String),\n `Residents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services (\n `resident_id` Nullable(Int64),\n `service_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `property_id` Nullable(Int64),\n `date_requested` Nullable(String),\n `date_provided` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Services_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Things (\n `thing_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `Type_of_Thing_Code` Nullable(String),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Things_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Things_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Timed_Locations_of_Things (\n `thing_id` Int64,\n `Date_and_Time` Date,\n `Location_Code` String\n);\nCREATE TABLE Timed_Status_of_Things (\n `thing_id` Int64,\n `Date_and_Date` Date,\n `Status_of_Thing_Code` String\n);" + }, + { + "db_id": "soccer_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A talented young football player known for his exceptional dribbling and quick acceleration.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Who are the top 5 football players known for exceptional dribbling and quick acceleration? Provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Football players who excel in dribbling and have remarkable speed.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top footballers famous for their dribbling skills and rapid acceleration.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players known for outstanding dribbling and quick bursts of speed.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Elite footballers recognized for dribbling prowess and fast acceleration.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Football stars with exceptional dribbling ability and swift movement.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Country (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE League (\n `id` Nullable(Int64),\n `country_id` Nullable(Int64),\n `name` Nullable(String),\n `League_description` Nullable(String),\n `League_description_embedding` Array(Float32)\n);\nCREATE TABLE Player (\n `id` Nullable(Int64),\n `player_api_id` Nullable(Int64),\n `player_name` Nullable(String),\n `player_fifa_api_id` Nullable(Int64),\n `birthday` Nullable(String),\n `height` Nullable(Int64),\n `weight` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Attributes (\n `id` Nullable(Int64),\n `player_fifa_api_id` Nullable(Int64),\n `player_api_id` Nullable(Int64),\n `date` Nullable(String),\n `overall_rating` Nullable(Int64),\n `potential` Nullable(Int64),\n `preferred_foot` Nullable(String),\n `attacking_work_rate` Nullable(String),\n `defensive_work_rate` Nullable(String),\n `crossing` Nullable(Int64),\n `finishing` Nullable(Int64),\n `heading_accuracy` Nullable(Int64),\n `short_passing` Nullable(Int64),\n `volleys` Nullable(Int64),\n `dribbling` Nullable(Int64),\n `curve` Nullable(Int64),\n `free_kick_accuracy` Nullable(Int64),\n `long_passing` Nullable(Int64),\n `ball_control` Nullable(Int64),\n `acceleration` Nullable(Int64),\n `sprint_speed` Nullable(Int64),\n `agility` Nullable(Int64),\n `reactions` Nullable(Int64),\n `balance` Nullable(Int64),\n `shot_power` Nullable(Int64),\n `jumping` Nullable(Int64),\n `stamina` Nullable(Int64),\n `strength` Nullable(Int64),\n `long_shots` Nullable(Int64),\n `aggression` Nullable(Int64),\n `interceptions` Nullable(Int64),\n `positioning` Nullable(Int64),\n `vision` Nullable(Int64),\n `penalties` Nullable(Int64),\n `marking` Nullable(Int64),\n `standing_tackle` Nullable(Int64),\n `sliding_tackle` Nullable(Int64),\n `gk_diving` Nullable(Int64),\n `gk_handling` Nullable(Int64),\n `gk_kicking` Nullable(Int64),\n `gk_positioning` Nullable(Int64),\n `gk_reflexes` Nullable(Int64),\n `Player_Attributes_description` Nullable(String)\n);\nCREATE TABLE Team (\n `id` Nullable(Int64),\n `team_api_id` Nullable(Int64),\n `team_fifa_api_id` Nullable(Int64),\n `team_long_name` Nullable(String),\n `team_short_name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Team_Attributes (\n `id` Nullable(Int64),\n `team_fifa_api_id` Nullable(Int64),\n `team_api_id` Nullable(Int64),\n `date` Nullable(String),\n `buildUpPlaySpeed` Nullable(Int64),\n `buildUpPlaySpeedClass` Nullable(String),\n `buildUpPlayDribbling` Nullable(Int64),\n `buildUpPlayDribblingClass` Nullable(String),\n `buildUpPlayPassing` Nullable(Int64),\n `buildUpPlayPassingClass` Nullable(String),\n `buildUpPlayPositioningClass` Nullable(String),\n `chanceCreationPassing` Nullable(Int64),\n `chanceCreationPassingClass` Nullable(String),\n `chanceCreationCrossing` Nullable(Int64),\n `chanceCreationCrossingClass` Nullable(String),\n `chanceCreationShooting` Nullable(Int64),\n `chanceCreationShootingClass` Nullable(String),\n `chanceCreationPositioningClass` Nullable(String),\n `defencePressure` Nullable(Int64),\n `defencePressureClass` Nullable(String),\n `defenceAggression` Nullable(Int64),\n `defenceAggressionClass` Nullable(String),\n `defenceTeamWidth` Nullable(Int64),\n `defenceTeamWidthClass` Nullable(String),\n `defenceDefenderLineClass` Nullable(String),\n `Team_Attributes_description` Nullable(String)\n);" + }, + { + "db_id": "college_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Introduction to Accounting, focuses on basic principles and practices') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance\nFROM CLASS\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which class code and section correspond to an introductory accounting class that focuses on basic principles and practices?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Introductory accounting course covering fundamental principles') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Basic accounting class on core practices and principles') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Principles of accounting introductory course') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Intro to accounting focusing on foundational principles and practices') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Accounting basics class with emphasis on principles and practices') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE CLASS (\n `CLASS_CODE` Nullable(String),\n `CRS_CODE` Nullable(String),\n `CLASS_SECTION` Nullable(String),\n `CLASS_TIME` Nullable(String),\n `CLASS_ROOM` Nullable(String),\n `PROF_NUM` Nullable(Int64),\n `CLASS_description` Nullable(String),\n `CLASS_description_embedding` Array(Float32)\n);\nCREATE TABLE COURSE (\n `CRS_CODE` Nullable(String),\n `DEPT_CODE` Nullable(String),\n `CRS_DESCRIPTION` Nullable(String),\n `CRS_CREDIT` Nullable(Float64),\n `CRS_DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE DEPARTMENT (\n `DEPT_CODE` Nullable(String),\n `DEPT_NAME` Nullable(String),\n `SCHOOL_CODE` Nullable(String),\n `EMP_NUM` Nullable(Int64),\n `DEPT_ADDRESS` Nullable(String),\n `DEPT_EXTENSION` Nullable(String),\n `DEPARTMENT_description` Nullable(String),\n `DEPARTMENT_description_embedding` Array(Float32)\n);\nCREATE TABLE EMPLOYEE (\n `EMP_NUM` Nullable(Int64),\n `EMP_LNAME` Nullable(String),\n `EMP_FNAME` Nullable(String),\n `EMP_INITIAL` Nullable(String),\n `EMP_JOBCODE` Nullable(String),\n `EMP_HIREDATE` Nullable(String),\n `EMP_DOB` Nullable(String),\n `EMPLOYEE_description` Nullable(String),\n `EMPLOYEE_description_embedding` Array(Float32)\n);\nCREATE TABLE ENROLL (\n `CLASS_CODE` Nullable(String),\n `STU_NUM` Nullable(Int64),\n `ENROLL_GRADE` Nullable(String)\n);\nCREATE TABLE PROFESSOR (\n `EMP_NUM` Nullable(Int64),\n `DEPT_CODE` Nullable(String),\n `PROF_OFFICE` Nullable(String),\n `PROF_EXTENSION` Nullable(String),\n `PROF_HIGH_DEGREE` Nullable(String),\n `PROFESSOR_description` Nullable(String),\n `PROFESSOR_description_embedding` Array(Float32)\n);\nCREATE TABLE STUDENT (\n `STU_NUM` Nullable(Int64),\n `STU_LNAME` Nullable(String),\n `STU_FNAME` Nullable(String),\n `STU_INIT` Nullable(String),\n `STU_DOB` Nullable(String),\n `STU_HRS` Nullable(Int64),\n `STU_CLASS` Nullable(String),\n `STU_GPA` Nullable(Float64),\n `STU_TRANSFER` Nullable(Float64),\n `DEPT_CODE` Nullable(String),\n `STU_PHONE` Nullable(String),\n `PROF_NUM` Nullable(Int64),\n `STUDENT_description` Nullable(String),\n `STUDENT_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "store_product", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high-performance scanner with robust output capabilities') AS ref_vec_0\n\nSELECT p.product_id, distance(p.product_description_embedding, ref_vec_0) AS distance\nFROM product p\nJOIN store_product sp ON toString(p.product_id) = toString(sp.Product_ID)\nJOIN store s ON toString(sp.Store_ID) = toString(s.Store_ID)\nWHERE s.Ranking < 5\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the product IDs for the top 3 products that match the idea of a high-performance scanner with great output capabilities? Oh, and make sure they're in stores with a ranking better than 5. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-notch scanner with excellent output performance') AS ref_vec_0\n\nSELECT p.product_id, distance(p.product_description_embedding, ref_vec_0) AS distance FROM product p JOIN store_product sp ON toString(p.product_id) = toString(sp.Product_ID) JOIN store s ON toString(sp.Store_ID) = toString(s.Store_ID) WHERE s.Ranking < 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-efficiency scanner with superior output features') AS ref_vec_0\n\nSELECT p.product_id, distance(p.product_description_embedding, ref_vec_0) AS distance FROM product p JOIN store_product sp ON toString(p.product_id) = toString(sp.Product_ID) JOIN store s ON toString(sp.Store_ID) = toString(s.Store_ID) WHERE s.Ranking < 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Premium scanner with outstanding output quality') AS ref_vec_0\n\nSELECT p.product_id, distance(p.product_description_embedding, ref_vec_0) AS distance FROM product p JOIN store_product sp ON toString(p.product_id) = toString(sp.Product_ID) JOIN store s ON toString(sp.Store_ID) = toString(s.Store_ID) WHERE s.Ranking < 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-performance scanner with exceptional output capabilities') AS ref_vec_0\n\nSELECT p.product_id, distance(p.product_description_embedding, ref_vec_0) AS distance FROM product p JOIN store_product sp ON toString(p.product_id) = toString(sp.Product_ID) JOIN store s ON toString(sp.Store_ID) = toString(s.Store_ID) WHERE s.Ranking < 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Efficient scanner with high-quality output') AS ref_vec_0\n\nSELECT p.product_id, distance(p.product_description_embedding, ref_vec_0) AS distance FROM product p JOIN store_product sp ON toString(p.product_id) = toString(sp.Product_ID) JOIN store s ON toString(sp.Store_ID) = toString(s.Store_ID) WHERE s.Ranking < 5\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'product_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE district (\n `District_ID` Nullable(Int64),\n `District_name` Nullable(String),\n `Headquartered_City` Nullable(String),\n `City_Population` Nullable(Float64),\n `City_Area` Nullable(Float64),\n `district_description` Nullable(String),\n `district_description_embedding` Array(Float32)\n);\nCREATE TABLE product (\n `product_id` Nullable(Int64),\n `product` Nullable(String),\n `dimensions` Nullable(String),\n `dpi` Nullable(Float64),\n `pages_per_minute_color` Nullable(Float64),\n `max_page_size` Nullable(String),\n `interface` Nullable(String),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE store (\n `Store_ID` Nullable(Int64),\n `Store_Name` Nullable(String),\n `Type` Nullable(String),\n `Area_size` Nullable(Float64),\n `Number_of_product_category` Nullable(Float64),\n `Ranking` Nullable(Int64),\n `store_description` Nullable(String),\n `store_description_embedding` Array(Float32)\n);\nCREATE TABLE store_district (\n `Store_ID` Nullable(Int64),\n `District_ID` Nullable(Int64)\n);\nCREATE TABLE store_product (\n `Store_ID` Nullable(Int64),\n `Product_ID` Nullable(Int64)\n);" + }, + { + "db_id": "network_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', '9th grader') AS ref_vec_0\n\nSELECT h.name, f.name AS friend_name, distance(h.Highschooler_description_embedding, ref_vec_0) AS distance\nFROM Highschooler h\nJOIN Friend fr ON toString(h.ID) = toString(fr.student_id)\nJOIN Highschooler f ON toString(fr.friend_id) = toString(f.ID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 8, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the names of up to 5 highschoolers who are most representative of a 9th grader and provide the names of their friends.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'typical ninth grader') AS ref_vec_0\n\nSELECT h.name, f.name AS friend_name, distance(h.Highschooler_description_embedding, ref_vec_0) AS distance FROM Highschooler h JOIN Friend fr ON toString(h.ID) = toString(fr.student_id) JOIN Highschooler f ON toString(fr.friend_id) = toString(f.ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'average 9th grade student') AS ref_vec_0\n\nSELECT h.name, f.name AS friend_name, distance(h.Highschooler_description_embedding, ref_vec_0) AS distance FROM Highschooler h JOIN Friend fr ON toString(h.ID) = toString(fr.student_id) JOIN Highschooler f ON toString(fr.friend_id) = toString(f.ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'representative freshman') AS ref_vec_0\n\nSELECT h.name, f.name AS friend_name, distance(h.Highschooler_description_embedding, ref_vec_0) AS distance FROM Highschooler h JOIN Friend fr ON toString(h.ID) = toString(fr.student_id) JOIN Highschooler f ON toString(fr.friend_id) = toString(f.ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'common 9th grader') AS ref_vec_0\n\nSELECT h.name, f.name AS friend_name, distance(h.Highschooler_description_embedding, ref_vec_0) AS distance FROM Highschooler h JOIN Friend fr ON toString(h.ID) = toString(fr.student_id) JOIN Highschooler f ON toString(fr.friend_id) = toString(f.ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'standard ninth grader') AS ref_vec_0\n\nSELECT h.name, f.name AS friend_name, distance(h.Highschooler_description_embedding, ref_vec_0) AS distance FROM Highschooler h JOIN Friend fr ON toString(h.ID) = toString(fr.student_id) JOIN Highschooler f ON toString(fr.friend_id) = toString(f.ID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column '_--h.Highschooler_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Friend (\n `student_id` Nullable(Int64),\n `friend_id` Nullable(Int64)\n);\nCREATE TABLE Highschooler (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `grade` Nullable(Int64),\n `Highschooler_description` Nullable(String),\n `Highschooler_description_embedding` Array(Float32)\n);\nCREATE TABLE Likes (\n `student_id` Nullable(Int64),\n `liked_id` Nullable(Int64)\n);" + }, + { + "db_id": "party_people", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Released in the United States with a focus on regional policies and governance.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance\nFROM region r\nJOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the names of regions and their corresponding political parties for the top 10 regions that are most aligned with the concept of being released in the United States with a focus on regional policies and governance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Regions in the US emphasizing regional governance and policy alignment.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'US regions with a strong focus on regional policy and governance.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top US regions aligned with policies focused on regional governance.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Regions prioritizing governance and policy within the US context.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'US regions where regional policies and governance are highly emphasized.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Member_Name` Nullable(String),\n `Party_ID` Nullable(String),\n `In_office` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Minister` Nullable(String),\n `Took_office` Nullable(String),\n `Left_office` Nullable(String),\n `Region_ID` Nullable(Int64),\n `Party_name` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);\nCREATE TABLE party_events (\n `Event_ID` Nullable(Int64),\n `Event_Name` Nullable(String),\n `Party_ID` Nullable(Int64),\n `Member_in_charge_ID` Nullable(Int64),\n `party_events_description` Nullable(String),\n `party_events_description_embedding` Array(Float32)\n);\nCREATE TABLE region (\n `Region_ID` Nullable(Int64),\n `Region_name` Nullable(String),\n `Date` Nullable(String),\n `Label` Nullable(String),\n `Format` Nullable(String),\n `Catalogue` Nullable(String),\n `region_description` Nullable(String),\n `region_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "race_track", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Daytona 500, a famous NASCAR race, happening in February at Track 3.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance \nFROM race\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you tell me which race seems to line up with the description of that well-known February NASCAR event at Track 3?", + "external_knowledge": "The use of vector embeddings, like the `all-MiniLM-L6-v2`, allows for the comparison of textual data by translating them into numerical vectors. The `MATCH` operator is used to find approximate nearest neighbors based on vector similarity, typically using Euclidean distance. In this context, the query is looking for textual similarities rather than exact matches, enabling more nuanced searches. The phrase \"well-known February NASCAR event\" refers to the Daytona 500, which is an iconic race that takes place annually in February.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The well-known NASCAR event in February at Track 3, commonly referred to as the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The famous February NASCAR race at Track 3, known as the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Track 3 hosts a major NASCAR event every February, famously called the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Daytona 500, a prominent NASCAR race occurring in February at Track 3.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In February, Track 3 features a renowned race known as the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE race (\n `Race_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Class` Nullable(String),\n `Date` Nullable(String),\n `Track_ID` Nullable(String),\n `race_description` Nullable(String),\n `race_description_embedding` Array(Float32)\n);\nCREATE TABLE track (\n `Track_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Location` Nullable(String),\n `Seating` Nullable(Float64),\n `Year_Opened` Nullable(Float64),\n `track_description` Nullable(String),\n `track_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "flight_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Long-haul flight from New York to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft suitable for long international flights') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance\n FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance\n FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName\nFROM SimilarFlights sf\nJOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid)\nORDER BY sf.distance, sa.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the ten flights that best match the description of a long-haul journey from New York to London and pair them with the five aircraft most suitable for long international flights. List the flight numbers and corresponding aircraft names, ordered by their similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Transatlantic journey from NYC to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft designed for long-haul international routes') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Extended distance flight from New York to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft optimal for long-distance international travel') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Long-distance flight from NYC to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft ideal for lengthy international journeys') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Intercontinental flight from New York to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft best for long international flights') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Long-haul journey across the Atlantic from NYC to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft suitable for extended international flights') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `aid` Nullable(Int64),\n `name` Nullable(String),\n `distance_val` Nullable(Int64),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE certificate (\n `eid` Nullable(String),\n `aid` Nullable(String)\n);\nCREATE TABLE employee (\n `eid` Nullable(Int64),\n `name` Nullable(String),\n `salary` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `flno` Nullable(Int64),\n `origin` Nullable(String),\n `destination` Nullable(String),\n `distance_val` Nullable(Int64),\n `departure_date` Nullable(String),\n `arrival_date` Nullable(String),\n `price` Nullable(Int64),\n `aid` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "dorm_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old student majoring in computer science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dorm with a capacity of 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance\n FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance\n FROM Dorm_filtered AS Dorm\n)\n\nSELECT \n s.StuID AS StuID, \n d.dormid AS dormid\nFROM \n StudentMatches s\nJOIN \n Lives_in l ON toString(s.StuID) = toString(l.stuid)\nJOIN \n DormMatches d ON toString(l.dormid) = toString(d.dormid)\nORDER BY \n s.student_distance + d.dorm_distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 4, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "**\n\nPlease provide the student IDs and dormitory IDs for the top 10 student-dormitory pairs where the students are described as 18-year-old computer science majors, and the dormitories are described as having a capacity for 100 male students.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old computer science student') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dormitory for 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'computer science major, 18 years old') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'accommodation for 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'student, 18, studying computer science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dorm for 100 males') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old major in CS') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'housing for 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old studying CS') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dormitory with capacity for 100 males') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Dorm (\n `dormid` Nullable(Int64),\n `dorm_name` Nullable(String),\n `student_capacity` Nullable(Int64),\n `gender` Nullable(String),\n `Dorm_description` Nullable(String),\n `Dorm_description_embedding` Array(Float32)\n);\nCREATE TABLE Dorm_amenity (\n `amenid` Nullable(Int64),\n `amenity_name` Nullable(String),\n `Dorm_amenity_description` Nullable(String),\n `Dorm_amenity_description_embedding` Array(Float32)\n);\nCREATE TABLE Has_amenity (\n `dormid` Nullable(Int64),\n `amenid` Nullable(Int64)\n);\nCREATE TABLE Lives_in (\n `stuid` Nullable(Int64),\n `dormid` Nullable(Int64),\n `room_number` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "machine_repair", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'experienced technician in the NY team') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'complex repair procedure for launch vehicle') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'high-performance motorcycle from Marlboro Pileri') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance\n FROM t_filtered AS t\n ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance\n FROM r_filtered AS r\n ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance\n FROM m_filtered AS m\n ORDER BY distance\n)\n\nSELECT ra.technician_id\nFROM repair_assignment ra\nJOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id)\nJOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID)\nJOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID)\nORDER BY tm.distance + rm.distance + mm.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Who is the technician assigned to a repair involving a high-performance Marlboro Pileri motorcycle and a complex launch vehicle procedure in the NY team?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'technician specializing in NY high-performance vehicle repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'detailed procedure for complex launch vehicle repair') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri high-speed motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'NY team technician with expertise in complex repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'launch vehicle repair involving complex procedures') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'high-performance Marlboro Pileri motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'expert technician from NY specializing in vehicle procedures') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'complex launch vehicle repair task') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri high-performance motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'NY-based technician for high-performance vehicle repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'repair procedure for complex launch vehicle') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri motorcycle with high performance') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'technician in NY team skilled in high-performance repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'complex procedures for launch vehicle repair') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri high-performance motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE machine (\n `Machine_ID` Nullable(Int64),\n `Making_Year` Nullable(Int64),\n `Class` Nullable(String),\n `Team` Nullable(String),\n `Machine_series` Nullable(String),\n `value_points` Nullable(Float64),\n `quality_rank` Nullable(Int64),\n `machine_description` Nullable(String),\n `machine_description_embedding` Array(Float32)\n);\nCREATE TABLE machine_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE repair (\n `repair_ID` Nullable(Int64),\n `name` Nullable(String),\n `Launch_Date` Nullable(String),\n `Notes` Nullable(String),\n `repair_description` Nullable(String),\n `Notes_embedding` Array(Float32),\n `repair_description_embedding` Array(Float32)\n);\nCREATE TABLE repair_assignment (\n `technician_id` Nullable(Int64),\n `repair_ID` Nullable(Int64),\n `Machine_ID` Nullable(Int64)\n);\nCREATE TABLE repair_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE repair_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE technician (\n `technician_id` Nullable(Float64),\n `Name` Nullable(String),\n `Team` Nullable(String),\n `Starting_Year` Nullable(Float64),\n `Age` Nullable(Int64),\n `technician_description` Nullable(String),\n `technician_description_embedding` Array(Float32)\n);\nCREATE TABLE technician_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "music_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A soulful melody with deep lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance\n FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance\n FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance\n FROM files_filtered AS files\n)\n\nSELECT s.song_name\nFROM SimilarSongs s\nJOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name)\nJOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id)\nORDER BY s.song_distance + g.genre_distance + f.file_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you identify the song that best fits a \"soulful melody with deep lyrics,\" belongs to a \"popular genre with high rating,\" and is associated with a \"high-quality audio file with long duration\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A heartfelt tune with profound words') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An emotional melody with meaningful lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A moving melody with deep and thoughtful lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n WHERE song_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'A moving melody with deep AND thoughtful lyrics')\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A song with a soulful tune and insightful lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n WHERE song_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'A song with a soulful tune AND insightful lyrics')\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A touching melody with significant lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `artist_name` Nullable(String),\n `country` Nullable(String),\n `gender` Nullable(String),\n `preferred_genre` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE files (\n `f_id` Nullable(Int64),\n `artist_name` Nullable(String),\n `file_size` Nullable(String),\n `duration` Nullable(String),\n `formats` Nullable(String),\n `files_description` Nullable(String),\n `files_description_embedding` Array(Float32)\n);\nCREATE TABLE genre (\n `g_name` Nullable(String),\n `rating` Nullable(String),\n `most_popular_in` Nullable(String),\n `genre_description` Nullable(String),\n `genre_description_embedding` Array(Float32)\n);\nCREATE TABLE song (\n `song_name` Nullable(String),\n `artist_name` Nullable(String),\n `country` Nullable(String),\n `f_id` Nullable(Int64),\n `genre_is` Nullable(String),\n `rating` Nullable(Int64),\n `languages` Nullable(String),\n `releasedate` Nullable(String),\n `resolution` Nullable(Int64),\n `song_description` Nullable(String),\n `song_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "tracking_software_problems", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Dedicated technical support and problem-solving expertise.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date\n FROM Problem_Log\n WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance\nFROM Staff s\nJOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to identify the first names of the top 10 staff members who have recently been assigned to problem logs within the last 30 days and whose profiles closely match the description of dedicated technical support and problem-solving expertise.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Expert in technical support and problem resolution.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Technical support specialist with problem-solving skills.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Proficient in handling technical issues and solutions.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Skilled in technical troubleshooting and support.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dedicated to resolving technical problems efficiently.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "tracking_software_problems", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A query about interface design issues') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance \nFROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a problem category that has something to do with interface design challenges?", + "external_knowledge": "The `MATCH` operator in the query utilizes vector embedding for performing a semantic similarity search, which is not based on exact matches but rather on capturing the meaning and context of the input text. The embeddings are typically compared using Euclidean distance, where a smaller distance indicates higher similarity. The `lembed()` function generates vector embeddings using the model 'all-MiniLM-L6-v2' from a given text input to enable this search. The query limits the result to the single most relevant entry, highlighting the top problem category related to interface design issues.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Interface design challenges') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Issues related to UI design') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Problems in user interface creation') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Challenges in designing interfaces') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Design difficulties in UI development') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "medicine_enzyme_interaction", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase enzyme found in mitochondrion') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An FDA approved medicine used for treatment') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance\n FROM e_filtered AS e\n ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance\n FROM m_filtered AS m\n ORDER BY m.distance\n)\n\nSELECT mei.interaction_type\nFROM medicine_enzyme_interaction mei\nJOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id)\nJOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id)\nORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you please identify the type of interaction between the top 5 enzymes characterized as \"ALA synthase enzyme found in mitochondrion\" and the top 5 FDA-approved medicines used for treatment? Please ensure that the interaction is the most relevant based on the average similarity of their descriptions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase located in mitochondria') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A treatment-related FDA approved drug') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Mitochondrial ALA synthase enzyme') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA approved medication for therapy') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase enzyme within mitochondria') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA approved therapeutic medicine') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Mitochondria ALA synthase enzyme') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA authorized drug for treatment') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase enzyme found in mitochondrial region') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA sanctioned medicine for therapy') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE enzyme (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Location` Nullable(String),\n `Product` Nullable(String),\n `Chromosome` Nullable(String),\n `OMIM` Nullable(Int64),\n `Porphyria` Nullable(String),\n `enzyme_description` Nullable(String),\n `enzyme_description_embedding` Array(Float32)\n);\nCREATE TABLE medicine (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Trade_Name` Nullable(String),\n `FDA_approved` Nullable(String),\n `medicine_description` Nullable(String),\n `medicine_description_embedding` Array(Float32)\n);\nCREATE TABLE medicine_enzyme_interaction (\n `enzyme_id` Nullable(Int64),\n `medicine_id` Nullable(Int64),\n `interaction_type` Nullable(String)\n);" + }, + { + "db_id": "film_rank", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A science fiction film about space exploration.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A market covering a large number of cities.') AS ref_vec_1,\n\nfilm_filtered AS (\n SELECT\n *,\n distance(film_description_embedding, ref_vec_0) AS distance\n FROM film\n\n ORDER BY distance\n LIMIT 5\n),\n\nmarket_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM market\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilmSelection AS (\n SELECT Film_ID, Title, distance AS film_distance\n FROM film_filtered AS film BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance\n FROM market_filtered AS market BY market_distance\n)\n\nSELECT f.Title, m.Country\nFROM FilmSelection f\nJOIN film_market_estimation e ON toString(f.Film_ID) = toString(e.Film_ID)\nJOIN MarketSelection m ON toString(e.Market_ID) = toString(m.Market_ID)\nWHERE e.Year = 2023\nORDER BY f.film_distance + m.market_distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you please find the top 5 science fiction films about space exploration and the top 5 markets covering a large number of cities, and then tell me which of these films are estimated for those markets in 2023? I need the film titles and the countries, ordered by their combined similarity score, but only the best 10 results!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A sci-fi movie focused on space missions.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A market including numerous urban areas.') AS ref_vec_1,\n\nfilm_filtered AS (\n SELECT\n *,\n distance(film_description_embedding, ref_vec_0) AS distance\n FROM film\n\n ORDER BY distance\n LIMIT 5\n),\n\nmarket_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM market\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilmSelection AS (\n SELECT Film_ID, Title, distance AS film_distance FROM film_filtered AS film BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance FROM market_filtered AS market BY market_distance\n)\n\nSELECT f.Title, m.Country FROM FilmSelection f JOIN film_market_estimation e ON toString(f.Film_ID) = toString(e.Film_ID) JOIN MarketSelection m ON toString(e.Market_ID) = toString(m.Market_ID) WHERE e.Year = 2023 ORDER BY f.film_distance + m.market_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A futuristic film exploring the cosmos.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A marketplace reaching a wide range of cities.') AS ref_vec_1,\n\nfilm_filtered AS (\n SELECT\n *,\n distance(film_description_embedding, ref_vec_0) AS distance\n FROM film\n\n ORDER BY distance\n LIMIT 5\n),\n\nmarket_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM market\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilmSelection AS (\n SELECT Film_ID, Title, distance AS film_distance FROM film_filtered AS film BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance FROM market_filtered AS market BY market_distance\n)\n\nSELECT f.Title, m.Country FROM FilmSelection f JOIN film_market_estimation e ON toString(f.Film_ID) = toString(e.Film_ID) JOIN MarketSelection m ON toString(e.Market_ID) = toString(m.Market_ID) WHERE e.Year = 2023 ORDER BY f.film_distance + m.market_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A narrative about space travel in science fiction.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A market encompassing many metropolitan areas.') AS ref_vec_1,\n\nfilm_filtered AS (\n SELECT\n *,\n distance(film_description_embedding, ref_vec_0) AS distance\n FROM film\n\n ORDER BY distance\n LIMIT 5\n),\n\nmarket_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM market\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilmSelection AS (\n SELECT Film_ID, Title, distance AS film_distance FROM film_filtered AS film BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance FROM market_filtered AS market BY market_distance\n)\n\nSELECT f.Title, m.Country FROM FilmSelection f JOIN film_market_estimation e ON toString(f.Film_ID) = toString(e.Film_ID) JOIN MarketSelection m ON toString(e.Market_ID) = toString(m.Market_ID) WHERE e.Year = 2023 ORDER BY f.film_distance + m.market_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An adventure in space within the sci-fi genre.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A network of markets covering diverse cities.') AS ref_vec_1,\n\nfilm_filtered AS (\n SELECT\n *,\n distance(film_description_embedding, ref_vec_0) AS distance\n FROM film\n\n ORDER BY distance\n LIMIT 5\n),\n\nmarket_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM market\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilmSelection AS (\n SELECT Film_ID, Title, distance AS film_distance FROM film_filtered AS film BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance FROM market_filtered AS market BY market_distance\n)\n\nSELECT f.Title, m.Country FROM FilmSelection f JOIN film_market_estimation e ON toString(f.Film_ID) = toString(e.Film_ID) JOIN MarketSelection m ON toString(e.Market_ID) = toString(m.Market_ID) WHERE e.Year = 2023 ORDER BY f.film_distance + m.market_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A sci-fi story about interstellar exploration.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A market with extensive urban coverage.') AS ref_vec_1,\n\nfilm_filtered AS (\n SELECT\n *,\n distance(film_description_embedding, ref_vec_0) AS distance\n FROM film\n\n ORDER BY distance\n LIMIT 5\n),\n\nmarket_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM market\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilmSelection AS (\n SELECT Film_ID, Title, distance AS film_distance FROM film_filtered AS film BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance FROM market_filtered AS market BY market_distance\n)\n\nSELECT f.Title, m.Country FROM FilmSelection f JOIN film_market_estimation e ON toString(f.Film_ID) = toString(e.Film_ID) JOIN MarketSelection m ON toString(e.Market_ID) = toString(m.Market_ID) WHERE e.Year = 2023 ORDER BY f.film_distance + m.market_distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17349 ('BY') (line 27, col 36): BY film_distance\n),\n\nMarketSelection AS (\n SELECT Market_ID, Country, distance AS market_distance\n FROM market_filtered AS market BY market_distance\n). Expected one of: FINAL, SAMPLE, table, table function, subquery or list of joined tables, array join, LEFT ARRAY JOIN, INNER, ARRAY JOIN, GLOBAL, LOCAL, ANY, ALL, ASOF, SEMI, ANTI, ONLY, LEFT, RIGHT, FULL, CROSS, PASTE, JOIN, PREWHERE, WHERE, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE film (\n `Film_ID` Nullable(Int64),\n `Title` Nullable(String),\n `Studio` Nullable(String),\n `Director` Nullable(String),\n `Gross_in_dollar` Nullable(Int64),\n `film_description` Nullable(String),\n `film_description_embedding` Array(Float32)\n);\nCREATE TABLE film_market_estimation (\n `Estimation_ID` Nullable(Int64),\n `Low_Estimate` Nullable(Float64),\n `High_Estimate` Nullable(Float64),\n `Film_ID` Nullable(Int64),\n `Type` Nullable(String),\n `Market_ID` Nullable(Int64),\n `Year` Nullable(Int64)\n);\nCREATE TABLE film_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE film_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE market (\n `Market_ID` Nullable(Int64),\n `Country` Nullable(String),\n `Number_cities` Nullable(Int64),\n `market_description` Nullable(String),\n `market_description_embedding` Array(Float32)\n);\nCREATE TABLE market_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE market_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE market_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE market_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE market_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE market_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE market_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "allergy_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is a common environmental allergy.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance \nFROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I want to know which allergy type is identified as most similar to the concept of pollen being a common environmental allergy.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is often recognized as a typical environmental allergen.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is frequently associated with environmental allergies.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is a prevalent environmental allergy trigger.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is commonly linked to environmental allergy reactions.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Environmental allergies often include pollen as a major factor.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Allergy_Type (\n `Allergy` Nullable(String),\n `AllergyType` Nullable(String),\n `Allergy_Type_description` Nullable(String),\n `Allergy_Type_description_embedding` Array(Float32)\n);\nCREATE TABLE Has_Allergy (\n `StuID` Nullable(Int64),\n `Allergy` Nullable(String)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Famous rock album with iconic songs') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance\nFROM Album a\nJOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the titles of the top 5 albums that are well-known for their iconic rock songs and the names of the artists who created them?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary rock albums with memorable tracks') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top rock albums known for their standout songs') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Iconic albums with famous rock songs') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned rock albums with classic hits') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Celebrated rock albums featuring iconic tracks') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "apartment_rentals", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John Doe, male, born on January 1, 1990') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Flat with 2 bathrooms, 3 bedrooms, and 5 rooms total.') AS ref_vec_1,\n\nGuests_filtered AS (\n SELECT\n *,\n distance(Guests_description_embedding, ref_vec_0) AS distance\n FROM Guests\n\n ORDER BY distance\n LIMIT 5\n),\n\nApartments_filtered AS (\n SELECT\n *,\n distance(Apartments_description_embedding, ref_vec_1) AS distance\n FROM Apartments\n WHERE Apartments_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Flat with 2 bathrooms, 3 bedrooms,\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS guest_distance\n FROM Guests_filtered AS Guests\n),\n\nApartmentMatch AS (\n SELECT apt_id, building_id, distance AS apt_distance\n FROM Apartments_filtered AS Apartments 5 rooms total.')\n),\n\nBookingDetails AS (\n SELECT ab.apt_booking_id, ab.apt_id, ab.guest_id, ab.booking_start_date, ab.booking_end_date\n FROM Apartment_Bookings ab\n JOIN GuestMatch gm ON toString(ab.guest_id) = toString(gm.guest_id)\n JOIN ApartmentMatch am ON toString(ab.apt_id) = toString(am.apt_id)\n WHERE ab.booking_status_code = 'ACTIVE'\n)\n\nSELECT \n bd.apt_booking_id AS apt_booking_id, \n bd.apt_id AS apt_id, \n bd.guest_id AS guest_id, \n bd.booking_start_date AS booking_start_date, \n bd.booking_end_date AS booking_end_date,\n gm.guest_distance AS guest_distance,\n am.apt_distance AS apt_distance\nFROM BookingDetails bd\nJOIN GuestMatch gm ON toString(bd.guest_id) = toString(gm.guest_id)\nJOIN ApartmentMatch am ON toString(bd.apt_id) = toString(am.apt_id)\nORDER BY gm.guest_distance, am.apt_distance\nLIMIT 10;", + "sql_result_column_count": 7, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you tell me about a few of those active bookings where the guest seems quite similar to John Doe and the apartment has the charm of a flat with 2 bathrooms, 3 bedrooms, and a total of 5 rooms?", + "external_knowledge": "In this context, 'a few' refers to the top 10 active bookings that match the criteria. The vector search mechanism (`MATCH ... lembed(...)`) is used to find the closest matches based on semantic similarity. The `lembed()` function uses the `all-MiniLM-L6-v2` model to represent descriptions as embeddings. Vector similarity searches are executed using approximate nearest neighbor (ANN) methods, where similarity is determined by Euclidean distance. A shorter distance indicates a closer match. This method allows for identifying entities that are semantically similar to the provided descriptions, effectively capturing nuanced likenesses beyond exact matches.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Similar to John Doe, male, born in early 90s') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Apartment with 2 baths, 3 beds, total 5 rooms') AS ref_vec_1,\n\nGuests_filtered AS (\n SELECT\n *,\n distance(Guests_description_embedding, ref_vec_0) AS distance\n FROM Guests\n\n ORDER BY distance\n LIMIT 5\n),\n\nApartments_filtered AS (\n SELECT\n *,\n distance(Apartments_description_embedding, ref_vec_1) AS distance\n FROM Apartments\n\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS guest_distance FROM Guests_filtered AS Guests\n),\n\nApartmentMatch AS (\n SELECT apt_id, building_id, distance AS apt_distance FROM Apartments_filtered AS Apartments\n),\n\nBookingDetails AS (\n SELECT ab.apt_booking_id, ab.apt_id, ab.guest_id, ab.booking_start_date, ab.booking_end_date FROM Apartment_Bookings ab JOIN GuestMatch gm ON toString(ab.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(ab.apt_id) = toString(am.apt_id) WHERE ab.booking_status_code = 'ACTIVE'\n)\n\nSELECT bd.apt_booking_id, bd.apt_id, bd.guest_id, bd.booking_start_date, bd.booking_end_date, gm.guest_distance, am.apt_distance FROM BookingDetails bd JOIN GuestMatch gm ON toString(bd.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(bd.apt_id) = toString(am.apt_id) ORDER BY gm.guest_distance, am.apt_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Resembles John Doe, male, born January 1990') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Charming flat with 2 bathrooms, 3 bedrooms, 5 rooms') AS ref_vec_1,\n\nGuests_filtered AS (\n SELECT\n *,\n distance(Guests_description_embedding, ref_vec_0) AS distance\n FROM Guests\n\n ORDER BY distance\n LIMIT 5\n),\n\nApartments_filtered AS (\n SELECT\n *,\n distance(Apartments_description_embedding, ref_vec_1) AS distance\n FROM Apartments\n\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS guest_distance FROM Guests_filtered AS Guests\n),\n\nApartmentMatch AS (\n SELECT apt_id, building_id, distance AS apt_distance FROM Apartments_filtered AS Apartments\n),\n\nBookingDetails AS (\n SELECT ab.apt_booking_id, ab.apt_id, ab.guest_id, ab.booking_start_date, ab.booking_end_date FROM Apartment_Bookings ab JOIN GuestMatch gm ON toString(ab.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(ab.apt_id) = toString(am.apt_id) WHERE ab.booking_status_code = 'ACTIVE'\n)\n\nSELECT bd.apt_booking_id, bd.apt_id, bd.guest_id, bd.booking_start_date, bd.booking_end_date, gm.guest_distance, am.apt_distance FROM BookingDetails bd JOIN GuestMatch gm ON toString(bd.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(bd.apt_id) = toString(am.apt_id) ORDER BY gm.guest_distance, am.apt_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Guest like John Doe, male, born 1990') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Flat with 2 baths, 3 beds, 5 total rooms') AS ref_vec_1,\n\nGuests_filtered AS (\n SELECT\n *,\n distance(Guests_description_embedding, ref_vec_0) AS distance\n FROM Guests\n\n ORDER BY distance\n LIMIT 5\n),\n\nApartments_filtered AS (\n SELECT\n *,\n distance(Apartments_description_embedding, ref_vec_1) AS distance\n FROM Apartments\n\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS guest_distance FROM Guests_filtered AS Guests\n),\n\nApartmentMatch AS (\n SELECT apt_id, building_id, distance AS apt_distance FROM Apartments_filtered AS Apartments\n),\n\nBookingDetails AS (\n SELECT ab.apt_booking_id, ab.apt_id, ab.guest_id, ab.booking_start_date, ab.booking_end_date FROM Apartment_Bookings ab JOIN GuestMatch gm ON toString(ab.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(ab.apt_id) = toString(am.apt_id) WHERE ab.booking_status_code = 'ACTIVE'\n)\n\nSELECT bd.apt_booking_id, bd.apt_id, bd.guest_id, bd.booking_start_date, bd.booking_end_date, gm.guest_distance, am.apt_distance FROM BookingDetails bd JOIN GuestMatch gm ON toString(bd.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(bd.apt_id) = toString(am.apt_id) ORDER BY gm.guest_distance, am.apt_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John Doe lookalike, male, born in 1990s') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Apartment with charm, 2 bathrooms, 3 bedrooms, 5 rooms total') AS ref_vec_1,\n\nGuests_filtered AS (\n SELECT\n *,\n distance(Guests_description_embedding, ref_vec_0) AS distance\n FROM Guests\n\n ORDER BY distance\n LIMIT 5\n),\n\nApartments_filtered AS (\n SELECT\n *,\n distance(Apartments_description_embedding, ref_vec_1) AS distance\n FROM Apartments\n\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS guest_distance FROM Guests_filtered AS Guests\n),\n\nApartmentMatch AS (\n SELECT apt_id, building_id, distance AS apt_distance FROM Apartments_filtered AS Apartments\n),\n\nBookingDetails AS (\n SELECT ab.apt_booking_id, ab.apt_id, ab.guest_id, ab.booking_start_date, ab.booking_end_date FROM Apartment_Bookings ab JOIN GuestMatch gm ON toString(ab.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(ab.apt_id) = toString(am.apt_id) WHERE ab.booking_status_code = 'ACTIVE'\n)\n\nSELECT bd.apt_booking_id, bd.apt_id, bd.guest_id, bd.booking_start_date, bd.booking_end_date, gm.guest_distance, am.apt_distance FROM BookingDetails bd JOIN GuestMatch gm ON toString(bd.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(bd.apt_id) = toString(am.apt_id) ORDER BY gm.guest_distance, am.apt_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Similar guest to John Doe, male, born January 1, 1990') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Flat with 2 bathrooms, 3 bedrooms, charming 5 rooms') AS ref_vec_1,\n\nGuests_filtered AS (\n SELECT\n *,\n distance(Guests_description_embedding, ref_vec_0) AS distance\n FROM Guests\n\n ORDER BY distance\n LIMIT 5\n),\n\nApartments_filtered AS (\n SELECT\n *,\n distance(Apartments_description_embedding, ref_vec_1) AS distance\n FROM Apartments\n\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS guest_distance FROM Guests_filtered AS Guests\n),\n\nApartmentMatch AS (\n SELECT apt_id, building_id, distance AS apt_distance FROM Apartments_filtered AS Apartments\n),\n\nBookingDetails AS (\n SELECT ab.apt_booking_id, ab.apt_id, ab.guest_id, ab.booking_start_date, ab.booking_end_date FROM Apartment_Bookings ab JOIN GuestMatch gm ON toString(ab.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(ab.apt_id) = toString(am.apt_id) WHERE ab.booking_status_code = 'ACTIVE'\n)\n\nSELECT bd.apt_booking_id, bd.apt_id, bd.guest_id, bd.booking_start_date, bd.booking_end_date, gm.guest_distance, am.apt_distance FROM BookingDetails bd JOIN GuestMatch gm ON toString(bd.guest_id) = toString(gm.guest_id) JOIN ApartmentMatch am ON toString(bd.apt_id) = toString(am.apt_id) ORDER BY gm.guest_distance, am.apt_distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17221 ('MATCH') (line 20, col 44): MATCH lembed('all-MiniLM-L6-v2', 'Flat with 2 bathrooms, 3 bedrooms,\n ORDER BY distance\n LIMIT 5\n),\n\nGuestMatch AS (\n SELECT guest_id, distance AS gues. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Apartment_Bookings (\n `apt_booking_id` Int64,\n `apt_id` Nullable(Int64),\n `guest_id` Int64,\n `booking_status_code` String,\n `booking_start_date` Nullable(Date),\n `booking_end_date` Nullable(Date)\n);\nCREATE TABLE Apartment_Buildings (\n `building_id` Int64,\n `building_short_name` Nullable(String),\n `building_full_name` Nullable(String),\n `building_description` Nullable(String),\n `building_address` Nullable(String),\n `building_manager` Nullable(String),\n `building_phone` Nullable(String)\n);\nCREATE TABLE Apartment_Facilities (\n `apt_id` Int64,\n `facility_code` String\n);\nCREATE TABLE Apartments (\n `apt_id` Nullable(Int64),\n `building_id` Nullable(Int64),\n `apt_type_code` Nullable(String),\n `apt_number` Nullable(String),\n `bathroom_count` Nullable(Int64),\n `bedroom_count` Nullable(Int64),\n `room_count` Nullable(String),\n `Apartments_description` Nullable(String),\n `Apartments_description_embedding` Array(Float32)\n);\nCREATE TABLE Apartments_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Apartments_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Guests (\n `guest_id` Nullable(Int64),\n `gender_code` Nullable(String),\n `guest_first_name` Nullable(String),\n `guest_last_name` Nullable(String),\n `date_of_birth` Nullable(String),\n `Guests_description` Nullable(String),\n `Guests_description_embedding` Array(Float32)\n);\nCREATE TABLE Guests_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Guests_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE View_Unit_Status (\n `apt_id` Nullable(Int64),\n `apt_booking_id` Nullable(Int64),\n `status_date` Date,\n `available_yn` Nullable(String)\n);" + }, + { + "db_id": "university_basketball", + "sql": "SELECT u.School, COUNT(b.Team_ID) AS Total_Teams\nFROM university u\nJOIN basketball_match b ON toString(u.School_ID) = toString(b.School_ID)\nWHERE u.Primary_conference LIKE 'ACC%'\nGROUP BY u.School\nHAVING COUNT(b.Team_ID) > 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "For ACC conference schools, find those with more than one basketball team and return the school names and their total team count.", + "external_knowledge": "", + "sql_candidate": [ + "SELECT u.School, COUNT(b.Team_ID) AS Total_Teams\nFROM university u\nJOIN basketball_match b ON toString(u.School_ID) = toString(b.School_ID)\nWHERE u.Primary_conference LIKE 'ACC%'\nGROUP BY u.School\nHAVING COUNT(b.Team_ID) > 1;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE basketball_match (\n `Team_ID` Nullable(Int64),\n `School_ID` Nullable(Int64),\n `Team_Name` Nullable(String),\n `ACC_Regular_Season` Nullable(String),\n `ACC_Percent` Nullable(String),\n `ACC_Home` Nullable(String),\n `ACC_Road` Nullable(String),\n `All_Games` Nullable(String),\n `All_Games_Percent` Nullable(Int64),\n `All_Home` Nullable(String),\n `All_Road` Nullable(String),\n `All_Neutral` Nullable(String),\n `basketball_match_description` Nullable(String)\n);\nCREATE TABLE university (\n `School_ID` Nullable(Int64),\n `School` Nullable(String),\n `Location` Nullable(String),\n `Founded` Nullable(Float64),\n `Affiliation` Nullable(String),\n `Enrollment` Nullable(Float64),\n `Nickname` Nullable(String),\n `Primary_conference` Nullable(String),\n `university_description` Nullable(String)\n);" + }, + { + "db_id": "culture_company", + "sql": "WITH BookClubMovies AS (\n SELECT \n cc.Company_name AS Company_name, \n bc.Book_Title AS Book_Title, \n m.Gross_worldwide AS Gross_worldwide\n FROM \n culture_company cc\n INNER JOIN \n book_club bc ON toString(cc.book_club_id) = toString(bc.book_club_id)\n INNER JOIN \n movie m ON toString(cc.movie_id) = toString(m.movie_id)\n)\nSELECT \n bc.Book_Title AS Book_Title, \n AVG(bcm.Gross_worldwide) AS Avg_Gross\nFROM \n BookClubMovies bcm\nINNER JOIN \n book_club bc ON toString(bcm.Book_Title) = toString(bc.Book_Title)\nGROUP BY \n bc.Book_Title;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please calculate the average worldwide gross revenue for each book title associated with any book club? I need to know this for all the movies related to book clubs!", + "external_knowledge": "", + "sql_candidate": [ + "WITH BookClubMovies AS (\n SELECT \n cc.Company_name AS Company_name, \n bc.Book_Title AS Book_Title, \n m.Gross_worldwide AS Gross_worldwide\n FROM \n culture_company cc\n INNER JOIN \n book_club bc ON toString(cc.book_club_id) = toString(bc.book_club_id)\n INNER JOIN \n movie m ON toString(cc.movie_id) = toString(m.movie_id)\n)\nSELECT \n bc.Book_Title AS Book_Title, \n AVG(bcm.Gross_worldwide) AS Avg_Gross\nFROM \n BookClubMovies bcm\nINNER JOIN \n book_club bc ON toString(bcm.Book_Title) = toString(bc.Book_Title)\nGROUP BY \n bc.Book_Title;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE book_club (\n `book_club_id` Nullable(Int64),\n `Year` Nullable(Int64),\n `Author_or_Editor` Nullable(String),\n `Book_Title` Nullable(String),\n `Publisher` Nullable(String),\n `Category` Nullable(String),\n `Result` Nullable(String),\n `book_club_description` Nullable(String)\n);\nCREATE TABLE culture_company (\n `Company_name` Nullable(String),\n `Type` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `book_club_id` Nullable(String),\n `movie_id` Nullable(String),\n `culture_company_description` Nullable(String)\n);\nCREATE TABLE movie (\n `movie_id` Nullable(Int64),\n `Title` Nullable(String),\n `Year` Nullable(Int64),\n `Director` Nullable(String),\n `Budget_million` Nullable(Float64),\n `Gross_worldwide` Nullable(Int64),\n `movie_description` Nullable(String)\n);" + }, + { + "db_id": "inn_1", + "sql": "SELECT r.roomName, SUM(res.Rate * r.basePrice) AS TotalRevenue\nFROM Rooms r\nJOIN Reservations res ON toString(r.RoomId) = toString(res.Room)\nGROUP BY r.roomName\nHAVING COUNT(res.Code) > 0;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please calculate the total revenue generated by each room and provide me with the room names and their corresponding total revenue amounts? Make sure to include only those rooms that have been booked at least once!", + "external_knowledge": "", + "sql_candidate": [ + "SELECT r.roomName, SUM(res.Rate * r.basePrice) AS TotalRevenue\nFROM Rooms r\nJOIN Reservations res ON toString(r.RoomId) = toString(res.Room)\nGROUP BY r.roomName\nHAVING COUNT(res.Code) > 0;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Reservations (\n `Code` Nullable(Int64),\n `Room` Nullable(String),\n `CheckIn` Nullable(String),\n `CheckOut` Nullable(String),\n `Rate` Nullable(Float64),\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Adults` Nullable(Int64),\n `Kids` Nullable(Int64),\n `Reservations_description` Nullable(String)\n);\nCREATE TABLE Rooms (\n `RoomId` Nullable(String),\n `roomName` Nullable(String),\n `beds` Nullable(Int64),\n `bedType` Nullable(String),\n `maxOccupancy` Nullable(Int64),\n `basePrice` Nullable(Int64),\n `decor` Nullable(String),\n `Rooms_description` Nullable(String)\n);" + }, + { + "db_id": "driving_school", + "sql": "WITH StaffBornAfter1990 AS (\n SELECT staff_id\n FROM Staff\n WHERE date_of_birth > '1990-01-01'\n),\nLessonsWithIdentifiedStaff AS (\n SELECT l.customer_id, l.staff_id\n FROM Lessons l\n JOIN StaffBornAfter1990 sba\n ON toString(l.staff_id) = toString(sba.staff_id)\n),\nCustomerPaymentsSummary AS (\n SELECT cp.customer_id, SUM(cp.amount_payment) AS total_payment\n FROM Customer_Payments cp\n JOIN LessonsWithIdentifiedStaff lwis\n ON toString(cp.customer_id) = toString(lwis.customer_id)\n GROUP BY cp.customer_id\n)\nSELECT c.first_name, c.last_name, cps.total_payment\nFROM Customers c\nJOIN CustomerPaymentsSummary cps\nON toString(c.customer_id) = toString(cps.customer_id);", + "sql_result_column_count": 3, + "sql_result_rows_count": 6, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Could you help me find out the first names and last names of customers who took lessons from staff born after January 1, 1990, and also let me know the total amount they’ve paid for these lessons? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH StaffBornAfter1990 AS (\n SELECT staff_id\n FROM Staff\n WHERE date_of_birth > '1990-01-01'\n),\nLessonsWithIdentifiedStaff AS (\n SELECT l.customer_id, l.staff_id\n FROM Lessons l\n JOIN StaffBornAfter1990 sba\n ON toString(l.staff_id) = toString(sba.staff_id)\n),\nCustomerPaymentsSummary AS (\n SELECT cp.customer_id, SUM(cp.amount_payment) AS total_payment\n FROM Customer_Payments cp\n JOIN LessonsWithIdentifiedStaff lwis\n ON toString(cp.customer_id) = toString(lwis.customer_id)\n GROUP BY cp.customer_id\n)\nSELECT c.first_name, c.last_name, cps.total_payment\nFROM Customers c\nJOIN CustomerPaymentsSummary cps\nON toString(c.customer_id) = toString(cps.customer_id);" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String)\n);\nCREATE TABLE Customer_Payments (\n `customer_id` Int64,\n `datetime_payment` Date,\n `payment_method_code` String,\n `amount_payment` Nullable(Float64)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_address_id` Int64,\n `customer_status_code` String,\n `date_became_customer` Nullable(Date),\n `date_of_birth` Nullable(Date),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `amount_outstanding` Nullable(Float64),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `cell_mobile_phone_number` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Lessons (\n `lesson_id` Nullable(Int64),\n `customer_id` Int64,\n `lesson_status_code` String,\n `staff_id` Nullable(Int64),\n `vehicle_id` Int64,\n `lesson_date` Nullable(Date),\n `lesson_time` Nullable(String),\n `price` Nullable(Float64),\n `Lessons_description` Nullable(String)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_address_id` Int64,\n `nickname` Nullable(String),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `date_of_birth` Nullable(Date),\n `date_joined_staff` Nullable(Date),\n `date_left_staff` Nullable(Date),\n `Staff_description` Nullable(String)\n);\nCREATE TABLE Vehicles (\n `vehicle_id` Nullable(Int64),\n `vehicle_details` Nullable(String),\n `Vehicles_description` Nullable(String)\n);" + }, + { + "db_id": "formula_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The 2015 Monaco Grand Prix was a thrilling race held in Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Monte Carlo Circuit, Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance\n FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance\n FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name\nFROM RaceCandidates r\nJOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId)\nORDER BY r.distance, c.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the top 5 race and circuit pairings that are closely tied to the thrilling 2015 Monaco Grand Prix in Monte Carlo and the Monte Carlo Circuit in Monaco? I want to see their names!", + "external_knowledge": "", + "sql_candidate": [ + "WITH RaceCandidates AS ( SELECT raceId, name, distance FROM races WHERE races_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Exciting 2015 Monaco F1 race in Monte Carlo.') AND k = 5 ), CircuitCandidates AS ( SELECT circuitId, name, distance FROM circuits WHERE circuits_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Monaco's Monte Carlo racing circuit') AND k = 5 ) SELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON r.raceId = c.circuitId ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '2015 Grand Prix in Monte Carlo, Monaco.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Famous Monte Carlo Circuit in Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thrilling 2015 Monaco GP at Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Monte Carlo Circuit in the heart of Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '2015 Monaco GP excitement in Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Iconic Monte Carlo Circuit, Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Monaco Grand Prix 2015 in Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Renowned Monte Carlo Circuit, Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE circuits (\n `circuitId` Nullable(Int64),\n `circuitRef` Nullable(String),\n `name` Nullable(String),\n `location` Nullable(String),\n `country` Nullable(String),\n `lat` Nullable(Float64),\n `lng` Nullable(Float64),\n `alt` Nullable(String),\n `url` Nullable(String),\n `circuits_description` Nullable(String),\n `circuits_description_embedding` Array(Float32)\n);\nCREATE TABLE constructorResults (\n `constructorResultsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `points` Nullable(Float64),\n `status` Nullable(String)\n);\nCREATE TABLE constructorStandings (\n `constructorStandingsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `points` Nullable(Float64),\n `position` Nullable(Int64),\n `positionText` Nullable(String),\n `wins` Nullable(Int64)\n);\nCREATE TABLE constructors (\n `constructorId` Nullable(Int64),\n `constructorRef` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `url` Nullable(String),\n `constructors_description` Nullable(String),\n `constructors_description_embedding` Array(Float32)\n);\nCREATE TABLE driverStandings (\n `driverStandingsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `points` Nullable(Float64),\n `position` Nullable(Int64),\n `positionText` Nullable(String),\n `wins` Nullable(Int64)\n);\nCREATE TABLE drivers (\n `driverId` Nullable(Int64),\n `driverRef` Nullable(String),\n `number` Nullable(String),\n `code` Nullable(String),\n `forename` Nullable(String),\n `surname` Nullable(String),\n `dob` Nullable(String),\n `nationality` Nullable(String),\n `url` Nullable(String),\n `drivers_description` Nullable(String),\n `drivers_description_embedding` Array(Float32)\n);\nCREATE TABLE lapTimes (\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `lap` Nullable(Int64),\n `position` Nullable(Int64),\n `time` Nullable(String),\n `milliseconds` Nullable(Int64)\n);\nCREATE TABLE pitStops (\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `stop` Nullable(Int64),\n `lap` Nullable(Int64),\n `time` Nullable(String),\n `duration` Nullable(String),\n `milliseconds` Nullable(Int64)\n);\nCREATE TABLE qualifying (\n `qualifyId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `number` Nullable(Int64),\n `position` Nullable(Int64),\n `q1` Nullable(String),\n `q2` Nullable(String),\n `q3` Nullable(String)\n);\nCREATE TABLE races (\n `raceId` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(Int64),\n `circuitId` Nullable(Int64),\n `name` Nullable(String),\n `date` Nullable(String),\n `time` Nullable(String),\n `url` Nullable(String),\n `races_description` Nullable(String),\n `races_description_embedding` Array(Float32)\n);\nCREATE TABLE results (\n `resultId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `number` Nullable(Int64),\n `grid` Nullable(Int64),\n `position` Nullable(String),\n `positionText` Nullable(String),\n `positionOrder` Nullable(Int64),\n `points` Nullable(Float64),\n `laps` Nullable(String),\n `time` Nullable(String),\n `milliseconds` Nullable(String),\n `fastestLap` Nullable(String),\n `rank` Nullable(String),\n `fastestLapTime` Nullable(String),\n `fastestLapSpeed` Nullable(String),\n `statusId` Nullable(Int64)\n);\nCREATE TABLE seasons (\n `year` Nullable(Int64),\n `url` Nullable(String),\n `seasons_description` Nullable(String),\n `seasons_description_embedding` Array(Float32)\n);\nCREATE TABLE status (\n `statusId` Nullable(Int64),\n `status` Nullable(String)\n);" + }, + { + "db_id": "news_report", + "sql": "WITH Average_Attendance AS (\n SELECT AVG(Event_Attendance) AS Avg_Attendance\n FROM event\n),\nExperienced_Young_Journalists AS (\n SELECT journalist_ID, Name, Age, Years_working\n FROM journalist\n WHERE Years_working > 10 OR Age < 30\n),\nEvents_Above_Average AS (\n SELECT Event_ID\n FROM event, Average_Attendance\n WHERE Event_Attendance > Avg_Attendance\n)\nSELECT j.Name\nFROM news_report nr\nJOIN Experienced_Young_Journalists j ON toString(nr.journalist_ID) = toString(j.journalist_ID)\nJOIN Events_Above_Average e ON toString(nr.Event_ID) = toString(e.Event_ID);", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the names of journalists who have either more than 10 years of work experience or are under 30 years old and have authored news reports on events that had an attendance above the calculated average.", + "external_knowledge": "", + "sql_candidate": [ + "WITH Average_Attendance AS (\n SELECT AVG(Event_Attendance) AS Avg_Attendance\n FROM event\n),\nExperienced_Young_Journalists AS (\n SELECT journalist_ID, Name, Age, Years_working\n FROM journalist\n WHERE Years_working > 10 OR Age < 30\n),\nEvents_Above_Average AS (\n SELECT Event_ID\n FROM event, Average_Attendance\n WHERE Event_Attendance > Avg_Attendance\n)\nSELECT j.Name\nFROM news_report nr\nJOIN Experienced_Young_Journalists j ON toString(nr.journalist_ID) = toString(j.journalist_ID)\nJOIN Events_Above_Average e ON toString(nr.Event_ID) = toString(e.Event_ID);" + ], + "integration_level": 0, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 386, server response: Code: 386. DB::Exception: There is no supertype for types String, UInt8 because some of them are String/FixedString/Enum and some of them are not: while executing 'FUNCTION less(Age : 2, 30 : 5) -> less(Age, 30) Nullable(UInt8) : 7'. (NO_COMMON_TYPE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE event (\n `Event_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Name` Nullable(String),\n `Event_Attendance` Nullable(Int64),\n `event_description` Nullable(String)\n);\nCREATE TABLE journalist (\n `journalist_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Nationality` Nullable(String),\n `Age` Nullable(String),\n `Years_working` Nullable(Int64),\n `journalist_description` Nullable(String)\n);\nCREATE TABLE news_report (\n `journalist_ID` Nullable(Int64),\n `Event_ID` Nullable(Int64),\n `Work_Type` Nullable(String)\n);" + }, + { + "db_id": "aircraft", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium: 123 Main St, Boston, MA. Capacity: 50,000. Home team: Patriots') AS ref_vec_0,\n\nMatchLocations AS (\n SELECT Location, Winning_Pilot, Winning_Aircraft, distance(match.match_description_embedding, ref_vec_0) AS distance\n FROM match\n ORDER BY distance\n LIMIT 5\n),\n\nWinningPilotsAircrafts AS (\n SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name\n FROM MatchLocations m\n JOIN pilot p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot)\n JOIN aircraft a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)\n),\n\nAssociatedAirports AS (\n SELECT ap.Airport_Name, COUNT(*) AS Association_Count\n FROM WinningPilotsAircrafts wpa\n JOIN airport_aircraft aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name)\n JOIN airport ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID)\n GROUP BY ap.Airport_Name\n ORDER BY Association_Count DESC\n)\n\nSELECT Airport_Name\nFROM AssociatedAirports\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which airport is most frequently associated with aircraft piloted by winners in matches that have something to do with a large stadium at 123 Main St, Boston, MA, home to the Patriots, and hosting about 50,000 people?", + "external_knowledge": "The query utilizes vector operations to perform a semantic search on match descriptions. The `MATCH` clause with `lembed()` uses an approximate nearest neighbor (ANN) search to find the top 5 match descriptions that closely resemble the provided text. This text embedding and search process allows for finding relevant matches based on nuanced textual descriptions rather than exact keyword matches. The `k=5` specifies that it should return the top 5 closest matches, and these are ranked by how semantically similar they are to the described stadium event. In this context, understanding that \"something to do with\" refers to these vector-based similarities is crucial for interpreting the question.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Large stadium located at 123 Main St, Boston, MA, hosting the Patriots with a capacity of 50,000') AS ref_vec_0,\n\nMatchLocations AS (\n SELECT Location, Winning_Pilot, Winning_Aircraft, distance(match.match_description_embedding, ref_vec_0) AS distance FROM match\n ORDER BY distance\n LIMIT 5\n),\n\nWinningPilotsAircrafts AS (\n SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name FROM MatchLocations m JOIN pilot p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot) JOIN aircraft a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)\n),\n\nAssociatedAirports AS (\n SELECT ap.Airport_Name, COUNT(*) AS Association_Count FROM WinningPilotsAircrafts wpa JOIN airport_aircraft aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name) JOIN airport ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID) GROUP BY ap.Airport_Name ORDER BY Association_Count DESC\n)\n\nSELECT Airport_Name FROM AssociatedAirports LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium at 123 Main St, Boston, home of the Patriots, seating 50,000') AS ref_vec_0,\n\nMatchLocations AS (\n SELECT Location, Winning_Pilot, Winning_Aircraft, distance(match.match_description_embedding, ref_vec_0) AS distance FROM match\n ORDER BY distance\n LIMIT 5\n),\n\nWinningPilotsAircrafts AS (\n SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name FROM MatchLocations m JOIN pilot p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot) JOIN aircraft a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)\n),\n\nAssociatedAirports AS (\n SELECT ap.Airport_Name, COUNT(*) AS Association_Count FROM WinningPilotsAircrafts wpa JOIN airport_aircraft aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name) JOIN airport ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID) GROUP BY ap.Airport_Name ORDER BY Association_Count DESC\n)\n\nSELECT Airport_Name FROM AssociatedAirports LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '123 Main St, Boston stadium, home to Patriots, 50,000 capacity') AS ref_vec_0,\n\nMatchLocations AS (\n SELECT Location, Winning_Pilot, Winning_Aircraft, distance(match.match_description_embedding, ref_vec_0) AS distance FROM match\n ORDER BY distance\n LIMIT 5\n),\n\nWinningPilotsAircrafts AS (\n SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name FROM MatchLocations m JOIN pilot p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot) JOIN aircraft a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)\n),\n\nAssociatedAirports AS (\n SELECT ap.Airport_Name, COUNT(*) AS Association_Count FROM WinningPilotsAircrafts wpa JOIN airport_aircraft aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name) JOIN airport ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID) GROUP BY ap.Airport_Name ORDER BY Association_Count DESC\n)\n\nSELECT Airport_Name FROM AssociatedAirports LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Boston stadium at 123 Main St, Patriots home, 50,000 seats') AS ref_vec_0,\n\nMatchLocations AS (\n SELECT Location, Winning_Pilot, Winning_Aircraft, distance(match.match_description_embedding, ref_vec_0) AS distance FROM match\n ORDER BY distance\n LIMIT 5\n),\n\nWinningPilotsAircrafts AS (\n SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name FROM MatchLocations m JOIN pilot p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot) JOIN aircraft a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)\n),\n\nAssociatedAirports AS (\n SELECT ap.Airport_Name, COUNT(*) AS Association_Count FROM WinningPilotsAircrafts wpa JOIN airport_aircraft aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name) JOIN airport ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID) GROUP BY ap.Airport_Name ORDER BY Association_Count DESC\n)\n\nSELECT Airport_Name FROM AssociatedAirports LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Patriots stadium at 123 Main St, Boston, capacity of 50,000') AS ref_vec_0,\n\nMatchLocations AS (\n SELECT Location, Winning_Pilot, Winning_Aircraft, distance(match.match_description_embedding, ref_vec_0) AS distance FROM match\n ORDER BY distance\n LIMIT 5\n),\n\nWinningPilotsAircrafts AS (\n SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name FROM MatchLocations m JOIN pilot p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot) JOIN aircraft a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)\n),\n\nAssociatedAirports AS (\n SELECT ap.Airport_Name, COUNT(*) AS Association_Count FROM WinningPilotsAircrafts wpa JOIN airport_aircraft aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name) JOIN airport ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID) GROUP BY ap.Airport_Name ORDER BY Association_Count DESC\n)\n\nSELECT Airport_Name FROM AssociatedAirports LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'Airport_Name' while processing query: 'WITH [0.04508825019001961, 0.005471078213304281, -0.020981287583708763, -0.04243433475494385, 0.015918489545583725, -0.010061302222311497, -0.1070307120680809, 0.021623438224196434, 0.006811744067817926, 0.04761188104748726, -0.08461949974298477, -0.03616737946867943, 0.010410899296402931, -0.029510732740163803, -0.032559268176555634, 0.011152097955346107, -0.02362130768597126, -0.050725676119327545, 0.01704987697303295, 0.05280853435397148, 0.04515232890844345, -0.004823383875191212, -0.08269549161195755, 0.06785530596971512, -0.011717760004103184, 0.1322106570005417, 0.037549491971731186, 0.1334950178861618, -0.08375813812017441, -0.056367844343185425, -0.019142046570777893, -0.08884068578481674, 0.09101077169179916, 0.013437939807772636, 0.0015311434399336576, 0.060856468975543976, -0.061840303242206573, -0.05855074152350426, 0.10362708568572998, 0.04885287955403328, 0.030413830652832985, -0.010843373835086823, 0.022073782980442047, 0.13964663445949554, 0.03720896318554878, 0.07595761865377426, -0.060935575515031815, 0.018253115937113762, 0.08504148572683334, 0.027324164286255836, 0.0582536906003952, 0.09621278941631317, -0.031205451115965843, 0.057448700070381165, 0.03738110512495041, 0.05289026349782944, -0.007925286889076233, 0.01696520484983921, 0.007181832566857338, 0.013676868751645088, 0.101579949259758, -0.045048292726278305, -0.011704675853252411, -0.010531471110880375, -0.015075398609042168, -0.04173862189054489, -0.06154140457510948, 0.026780767366290092, -0.027269402518868446, -0.05860844627022743, 0.07435797899961472, 0.0966653972864151, 0.08660002797842026, -0.040562085807323456, 0.05637943372130394, 0.13789980113506317, -0.03364896401762962, 0.033637676388025284, -0.010544119402766228, 0.011912034824490547, 0.07411020994186401, -0.059685271233320236, -0.033202994614839554, 0.07720872014760971, 0.027958812192082405, 0.04124895855784416, -0.07836323976516724, 0.1370660662651062, -0.03824758902192116, -0.0009362449636682868, -0.07544505596160889, 0.0276633407920599, -0.020530644804239273, 0.02063954435288906, -0.039524421095848083, 0.05311477929353714, -0.10678543895483017, -0.036759234964847565, -0.04111848399043083, 0.04267139732837677, 0.012407360598444939, 0.08190711587667465, 0.07430021464824677, 0.021524574607610703, 0.0345478430390358, -0.025195812806487083, -0.03630037605762482, 0.1107780709862709, -0.023266801610589027, 0.002198354806751013, 0.043141093105077744, 0.03644382953643799, -0.08220750838518143, 0.028857603669166565, -0.030929798260331154, 0.010404455475509167, 0.03153224289417267, 0.007974487729370594, 0.022189533337950706, -0.039602410048246384, 0.022454313933849335, -0.00017439830116927624, 0.030783459544181824, -0.04274975508451462, -0.04724172502756119, -0.038201868534088135, -0.04533964395523071, -2.0192845833226726e-33, 0.027536960318684578, -0.030695052817463875, -0.007262225262820721, 0.02007695659995079, -0.03824695199728012, -0.010211881250143051, -0.0686982125043869, 0.028336849063634872, -0.048226915299892426, -0.055370137095451355, 0.03673224896192551, -0.013599993661046028, -0.0036364512052387, -0.07213839143514633, 0.0034071397967636585, -0.09981151670217514, 0.023701995611190796, -0.005149872042238712, -0.030231710523366928, -0.061493612825870514, -0.0807696208357811, -0.05361367389559746, -0.058413490653038025, 0.0753776878118515, -0.05365787446498871, -0.023967983201146126, -0.02456810139119625, -0.03128110244870186, -0.0013864815700799227, -0.007900270633399487, 0.006931680720299482, 0.0212108064442873, 0.012055554427206516, -0.0178332831710577, 0.03709264472126961, -0.06013292074203491, 0.003255685791373253, -0.02335565723478794, -0.024648500606417656, -0.06607424467802048, -0.049561504274606705, -0.007877958007156849, 0.025914745405316353, 0.03407379239797592, -0.037074532359838486, 0.0417492501437664, -0.010150701738893986, -0.005180676002055407, 0.09161530435085297, -0.00794009119272232, 0.03207476809620857, -0.03584416210651398, -0.13981610536575317, -0.025363994762301445, 0.03323403373360634, -0.09384047240018845, -0.002567383460700512, 0.008530749939382076, 0.0030194635037332773, 0.04289612919092178, -0.022730927914381027, 0.014283052645623684, 0.0105085838586092, 0.03398636355996132, -0.03469804301857948, -0.011569103226065636, 0.005305058788508177, -0.04425722360610962, 0.05732385814189911, 0.01870172843337059, 0.11298106610774994, -0.027797289192676544, 0.04326796904206276, 0.05640765652060509, -0.08609884977340698, -0.02218940295279026, 0.04858860373497009, 0.042525023221969604, -0.0386284738779068, 0.04091969504952431, -0.0034807873889803886, -0.05604587495326996, -0.02238490805029869, 0.045276422053575516, 0.015336732380092144, 0.008187144063413143, -0.04358377680182457, -0.005624301265925169, -0.041885536164045334, -0.03177370876073837, -0.0350743792951107, -0.028671247884631157, -0.028261292725801468, -0.10772652924060822, -0.10896500200033188, 4.474795212070992e-34, 0.023013902828097343, 0.013612307608127594, -0.07020939886569977, -0.038039468228816986, -0.06274459511041641, -0.005942406598478556, 0.026244517415761948, 0.06303068995475769, 0.013434536755084991, 0.049858324229717255, -0.046849802136421204, 0.06320284307003021, 0.052946850657463074, 0.000642772123683244, -0.07445403933525085, 0.028841674327850342, 0.02630474418401718, -0.03322003781795502, 0.016522368416190147, -0.02239271253347397, 0.020653681829571724, -0.02625279128551483, 0.015125024132430553, 0.03546834737062454, 0.03131130337715149, 0.036638565361499786, -0.09790880978107452, 0.02532612532377243, -0.024972233921289444, 0.005407341755926609, 0.028150459751486778, -0.10038842260837555, -0.014627425000071526, 0.04893692582845688, -0.03538522496819496, 0.04063539206981659, 0.02704351581633091, 0.11673253029584885, 0.03282967209815979, -0.040683042258024216, 0.00038273452082648873, -0.03983032703399658, -0.01940532587468624, 0.023666338995099068, 0.03310474753379822, 0.00019597263599280268, 0.08113826811313629, -0.026842771098017693, 0.03142419084906578, 0.014964508824050426, -0.07290139049291611, 0.01415792666375637, -0.08726280182600021, 0.03770838677883148, -0.015661558136343956, 0.01938219740986824, -0.013131375424563885, 0.01519649289548397, -0.004462886601686478, -0.08205538988113403, 0.0369611456990242, 0.04921125993132591, -0.0927390605211258, 0.07238993048667908, -0.02056455798447132, 0.056016985327005386, -0.06271853297948837, -0.05722683668136597, -0.06351321190595627, 0.13366317749023438, -0.10435255616903305, -0.008991534821689129, 0.012445815838873386, 0.06314864009618759, -0.035641271620988846, 0.014577477239072323, -0.04604237154126167, 0.014711115509271622, 0.04184737056493759, 0.1051945760846138, -0.013464643619954586, 0.055287543684244156, -0.04208552837371826, -0.00011914635979337618, 0.02931572124361992, 0.008232814259827137, 0.07515579462051392, 0.07427817583084106, -0.058034319430589676, 0.013869239948689938, -0.02006467990577221, 0.07447562366724014, -0.004354115575551987, -0.12784285843372345, -0.008680231869220734, -2.2174715397227374e-8, 0.022092308849096298, 0.07376207411289215, -0.022713415324687958, -0.013095867820084095, 0.04443860054016113, -0.07331395149230957, 0.0392829030752182, 0.02596915327012539, 0.09032033383846283, -0.020615193992853165, -0.03850160166621208, -0.07527398318052292, -0.059297993779182434, -0.006520971190184355, -0.016295989975333214, 0.009516570717096329, -0.0574570931494236, -0.04417170584201813, -0.006404831074178219, -0.005091220606118441, -0.062775619328022, 0.012365839444100857, -0.08769938349723816, -0.006548229604959488, 0.029187554493546486, -0.026399511843919754, 0.01958104968070984, 0.015359942801296711, -0.03680478036403656, 0.01930837891995907, 0.057323433458805084, -0.007981856353580952, -0.10230135172605515, -0.10820901393890381, 0.03877631202340126, 0.01789514161646366, -0.05176808312535286, -0.06945278495550156, 0.0019131975714117289, -0.043203823268413544, 0.0031545101664960384, -0.07640476524829865, -0.07661514729261398, 0.05394363030791283, 0.062128402292728424, -0.009481103159487247, -0.02572418563067913, 0.01478548813611269, -0.05585003271698952, -0.12136764824390411, -0.004661312326788902, 0.06028441712260246, -0.051799751818180084, 0.017922066152095795, -0.0031202465761452913, 0.03363991156220436, 0.0697755366563797, 0.03470175340771675, 0.06764259934425354, -0.0028799946885555983, 0.007300708908587694, 0.0664902776479721, -0.02152707800269127, 0.04322691261768341] AS ref_vec_0, MatchLocations AS (WITH [0.04508825019001961, 0.005471078213304281, -0.020981287583708763, -0.04243433475494385, 0.015918489545583725, -0.010061302222311497, -0.1070307120680809, 0.021623438224196434, 0.006811744067817926, 0.04761188104748726, -0.08461949974298477, -0.03616737946867943, 0.010410899296402931, -0.029510732740163803, -0.032559268176555634, 0.011152097955346107, -0.02362130768597126, -0.050725676119327545, 0.01704987697303295, 0.05280853435397148, 0.04515232890844345, -0.004823383875191212, -0.08269549161195755, 0.06785530596971512, -0.011717760004103184, 0.1322106570005417, 0.037549491971731186, 0.1334950178861618, -0.08375813812017441, -0.056367844343185425, -0.019142046570777893, -0.08884068578481674, 0.09101077169179916, 0.013437939807772636, 0.0015311434399336576, 0.060856468975543976, -0.061840303242206573, -0.05855074152350426, 0.10362708568572998, 0.04885287955403328, 0.030413830652832985, -0.010843373835086823, 0.022073782980442047, 0.13964663445949554, 0.03720896318554878, 0.07595761865377426, -0.060935575515031815, 0.018253115937113762, 0.08504148572683334, 0.027324164286255836, 0.0582536906003952, 0.09621278941631317, -0.031205451115965843, 0.057448700070381165, 0.03738110512495041, 0.05289026349782944, -0.007925286889076233, 0.01696520484983921, 0.007181832566857338, 0.013676868751645088, 0.101579949259758, -0.045048292726278305, -0.011704675853252411, 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-0.006548229604959488, 0.029187554493546486, -0.026399511843919754, 0.01958104968070984, 0.015359942801296711, -0.03680478036403656, 0.01930837891995907, 0.057323433458805084, -0.007981856353580952, -0.10230135172605515, -0.10820901393890381, 0.03877631202340126, 0.01789514161646366, -0.05176808312535286, -0.06945278495550156, 0.0019131975714117289, -0.043203823268413544, 0.0031545101664960384, -0.07640476524829865, -0.07661514729261398, 0.05394363030791283, 0.062128402292728424, -0.009481103159487247, -0.02572418563067913, 0.01478548813611269, -0.05585003271698952, -0.12136764824390411, -0.004661312326788902, 0.06028441712260246, -0.051799751818180084, 0.017922066152095795, -0.0031202465761452913, 0.03363991156220436, 0.0697755366563797, 0.03470175340771675, 0.06764259934425354, -0.0028799946885555983, 0.007300708908587694, 0.0664902776479721, -0.02152707800269127, 0.04322691261768341] AS ref_vec_0 SELECT m.Location, p.Name AS Pilot_Name, a.Aircraft AS Aircraft_Name FROM MatchLocations AS m INNER JOIN pilot AS p ON toString(p.Pilot_Id) = toString(m.Winning_Pilot) INNER JOIN aircraft AS a ON toString(a.Aircraft_ID) = toString(m.Winning_Aircraft)), AssociatedAirports AS (WITH [0.04508825019001961, 0.005471078213304281, -0.020981287583708763, -0.04243433475494385, 0.015918489545583725, -0.010061302222311497, -0.1070307120680809, 0.021623438224196434, 0.006811744067817926, 0.04761188104748726, -0.08461949974298477, -0.03616737946867943, 0.010410899296402931, -0.029510732740163803, -0.032559268176555634, 0.011152097955346107, -0.02362130768597126, -0.050725676119327545, 0.01704987697303295, 0.05280853435397148, 0.04515232890844345, -0.004823383875191212, -0.08269549161195755, 0.06785530596971512, -0.011717760004103184, 0.1322106570005417, 0.037549491971731186, 0.1334950178861618, -0.08375813812017441, -0.056367844343185425, -0.019142046570777893, -0.08884068578481674, 0.09101077169179916, 0.013437939807772636, 0.0015311434399336576, 0.060856468975543976, 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-0.007981856353580952, -0.10230135172605515, -0.10820901393890381, 0.03877631202340126, 0.01789514161646366, -0.05176808312535286, -0.06945278495550156, 0.0019131975714117289, -0.043203823268413544, 0.0031545101664960384, -0.07640476524829865, -0.07661514729261398, 0.05394363030791283, 0.062128402292728424, -0.009481103159487247, -0.02572418563067913, 0.01478548813611269, -0.05585003271698952, -0.12136764824390411, -0.004661312326788902, 0.06028441712260246, -0.051799751818180084, 0.017922066152095795, -0.0031202465761452913, 0.03363991156220436, 0.0697755366563797, 0.03470175340771675, 0.06764259934425354, -0.0028799946885555983, 0.007300708908587694, 0.0664902776479721, -0.02152707800269127, 0.04322691261768341] AS ref_vec_0 SELECT ap.Airport_Name, count(*) AS Association_Count FROM WinningPilotsAircrafts AS wpa INNER JOIN airport_aircraft AS aa ON toString(aa.Aircraft_ID) = toString(wpa.Aircraft_Name) INNER JOIN airport AS ap ON toString(ap.Airport_ID) = toString(aa.Airport_ID) GROUP BY ap.Airport_Name ORDER BY Association_Count DESC) SELECT Airport_Name FROM AssociatedAirports LIMIT 1', required columns: 'Airport_Name' 'Airport_Name'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Aircraft` Nullable(String),\n `Description` Nullable(String),\n `Max_Gross_Weight` Nullable(String),\n `Total_disk_area` Nullable(String),\n `Max_disk_Loading` Nullable(String),\n `Description_embedding` Array(Float32)\n);\nCREATE TABLE aircraft_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE airport (\n `Airport_ID` Nullable(Int64),\n `Airport_Name` Nullable(String),\n `Total_Passengers` Nullable(Float64),\n `fld___Change_2007` Nullable(String),\n `International_Passengers` Nullable(Float64),\n `Domestic_Passengers` Nullable(Float64),\n `Transit_Passengers` Nullable(Float64),\n `Aircraft_Movements` Nullable(Float64),\n `Freight_Metric_Tonnes` Nullable(Float64),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE airport_aircraft (\n `ID` Nullable(Int64),\n `Airport_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64)\n);\nCREATE TABLE airport_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE match (\n `Round` Nullable(Float64),\n `Location` Nullable(String),\n `Country` Nullable(String),\n `Date` Nullable(String),\n `Fastest_Qualifying` Nullable(String),\n `Winning_Pilot` Nullable(String),\n `Winning_Aircraft` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE match_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE pilot (\n `Pilot_Id` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "city_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis with a large population and high GDP') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Please identify the top 5 cities that are characterized as bustling metropolises with large populations and high GDPs, and provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A large urban center with a significant population and strong economic performance') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Major city with high population density and substantial GDP') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A thriving city with a large populace and robust economy') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An economic hub with a vast population and high economic output') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A populous city with a dynamic economy and high GDP') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE city (\n `City_ID` Nullable(Int64),\n `City` Nullable(String),\n `Hanzi` Nullable(String),\n `Hanyu_Pinyin` Nullable(String),\n `Regional_Population` Nullable(Int64),\n `GDP` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE hosting_city (\n `Year` Nullable(Int64),\n `Match_ID` Nullable(Int64),\n `Host_City` Nullable(String)\n);\nCREATE TABLE match (\n `Match_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Score` Nullable(String),\n `Result` Nullable(String),\n `Competition` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE temperature (\n `City_ID` Nullable(Int64),\n `Jan` Nullable(Float64),\n `Feb` Nullable(Float64),\n `Mar` Nullable(Float64),\n `Apr` Nullable(Float64),\n `Jun` Nullable(Float64),\n `Jul` Nullable(Float64),\n `Aug` Nullable(Float64),\n `Sep` Nullable(Float64),\n `Oct` Nullable(Float64),\n `Nov` Nullable(Float64),\n `Dec` Nullable(Float64)\n);" + }, + { + "db_id": "city_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'metropolitan area with high population and economic activities') AS ref_vec_0,\n\nRankedCities AS (\n SELECT\n c.City_ID AS City_ID,\n c.City AS City,\n c.city_description AS city_description,\n distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM\n city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT\n h.Year AS Year,\n h.Match_ID AS Match_ID,\n h.Host_City AS Host_City\n FROM\n hosting_city h\n INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT\n rc.City AS City\nFROM\n RankedCities rc\nJOIN\n HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the grand tapestry of bustling cities, which urban centers resonate with the vibrancy of high population and economic activities, and have also hosted magnificent gatherings?", + "external_knowledge": "The SQL query employs vector operations to perform an approximate nearest neighbor (ANN) search using the `MATCH` operator. This search aims to identify the top 5 cities whose descriptions are most similar to the concept of a \"metropolitan area with high population and economic activities\", implying these are cities with significant population density and economic vibrancy. The `lembed('all-MiniLM-L6-v2', ...)` function utilizes embeddings to evaluate similarity based on Euclidean distance, with closer distances indicating higher similarity. This technique is useful for finding entities that conceptually align with specified criteria, such as identifying major urban centers in this context.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'urban centers with large populations and thriving economies') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'cities known for bustling economic activities and significant gatherings') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'major urban areas with vibrant populations and events') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'cities with significant population and economic vibrancy hosting events') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'urban areas bustling with population and economic activities hosting gatherings') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE city (\n `City_ID` Nullable(Int64),\n `City` Nullable(String),\n `Hanzi` Nullable(String),\n `Hanyu_Pinyin` Nullable(String),\n `Regional_Population` Nullable(Int64),\n `GDP` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE hosting_city (\n `Year` Nullable(Int64),\n `Match_ID` Nullable(Int64),\n `Host_City` Nullable(String)\n);\nCREATE TABLE match (\n `Match_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Score` Nullable(String),\n `Result` Nullable(String),\n `Competition` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE temperature (\n `City_ID` Nullable(Int64),\n `Jan` Nullable(Float64),\n `Feb` Nullable(Float64),\n `Mar` Nullable(Float64),\n `Apr` Nullable(Float64),\n `Jun` Nullable(Float64),\n `Jul` Nullable(Float64),\n `Aug` Nullable(Float64),\n `Sep` Nullable(Float64),\n `Oct` Nullable(Float64),\n `Nov` Nullable(Float64),\n `Dec` Nullable(Float64)\n);" + }, + { + "db_id": "aircraft", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Experienced pilot with international flying experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'International air race with high competition level') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance\n FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft \nFROM m_filtered AS m\nJOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name)\nORDER BY p.distance\nLIMIT 5;", + "sql_result_column_count": 8, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you identify a handful of air races in the US, where the winners are some of those really seasoned pilots with global flight experience, and tell me about the races, including the pilots and planes involved?", + "external_knowledge": "The SQL query employs vector operations to perform semantic searches using text embeddings. The `MATCH` operator in combination with `lembed()` finds records that are most similar to a given textual description by utilizing approximate nearest neighbor (ANN) search. The parameter `k` specifies the number of similar items to return, with the results being ranked by similarity. In this context, \"Experienced pilot with international flying experience\" and \"International air race with high competition level\" are the key descriptions guiding the semantic searches. The Euclidean distance is used as a metric for similarity, where a smaller distance indicates a closer match. This allows for flexible and nuanced retrieval based on conceptual similarity rather than exact keyword matching.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Veteran pilot with global flight credentials') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Competitive air race with seasoned participants') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pilot with extensive international aviation experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'High-level air race with experienced pilots') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Globally experienced pilot with vast flight history') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Internationally renowned air race with elite pilots') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pilot with significant global flight experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Prestigious air race featuring skilled pilots') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pilot with extensive global flying background') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Air race with top-tier pilots and international acclaim') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE match_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Air race with top-tier pilots AND international acclaim') AND Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Aircraft` Nullable(String),\n `Description` Nullable(String),\n `Max_Gross_Weight` Nullable(String),\n `Total_disk_area` Nullable(String),\n `Max_disk_Loading` Nullable(String),\n `Description_embedding` Array(Float32)\n);\nCREATE TABLE aircraft_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE airport (\n `Airport_ID` Nullable(Int64),\n `Airport_Name` Nullable(String),\n `Total_Passengers` Nullable(Float64),\n `fld___Change_2007` Nullable(String),\n `International_Passengers` Nullable(Float64),\n `Domestic_Passengers` Nullable(Float64),\n `Transit_Passengers` Nullable(Float64),\n `Aircraft_Movements` Nullable(Float64),\n `Freight_Metric_Tonnes` Nullable(Float64),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE airport_aircraft (\n `ID` Nullable(Int64),\n `Airport_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64)\n);\nCREATE TABLE airport_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE match (\n `Round` Nullable(Float64),\n `Location` Nullable(String),\n `Country` Nullable(String),\n `Date` Nullable(String),\n `Fastest_Qualifying` Nullable(String),\n `Winning_Pilot` Nullable(String),\n `Winning_Aircraft` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE match_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE pilot (\n `Pilot_Id` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "imdb", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Inception') AS ref_vec_0\n\nSELECT m.title, g.genre, distance(m.title_embedding, ref_vec_0) AS distance\nFROM movie m\nJOIN classification c ON toString(m.mid) = toString(c.msid)\nJOIN genre g ON toString(c.gid) = toString(g.gid)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to find the titles and corresponding genres of the top 5 movies that are most similar to the movie \"Inception.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'movies similar to Inception') AS ref_vec_0\n\nSELECT m.title, g.genre, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN classification c ON toString(m.mid) = toString(c.msid) JOIN genre g ON toString(c.gid) = toString(g.gid)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'films like Inception') AS ref_vec_0\n\nSELECT m.title, g.genre, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN classification c ON toString(m.mid) = toString(c.msid) JOIN genre g ON toString(c.gid) = toString(g.gid)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top movies resembling Inception') AS ref_vec_0\n\nSELECT m.title, g.genre, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN classification c ON toString(m.mid) = toString(c.msid) JOIN genre g ON toString(c.gid) = toString(g.gid)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'cinematic works similar to Inception') AS ref_vec_0\n\nSELECT m.title, g.genre, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN classification c ON toString(m.mid) = toString(c.msid) JOIN genre g ON toString(c.gid) = toString(g.gid)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'movies with themes like Inception') AS ref_vec_0\n\nSELECT m.title, g.genre, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN classification c ON toString(m.mid) = toString(c.msid) JOIN genre g ON toString(c.gid) = toString(g.gid)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'title_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `aid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `actor_description` Nullable(String)\n);\nCREATE TABLE cast (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `aid` Nullable(Int64),\n `role` Nullable(Int64)\n);\nCREATE TABLE classification (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `gid` Nullable(Int64)\n);\nCREATE TABLE company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `country_code` Nullable(String),\n `company_description` Nullable(String)\n);\nCREATE TABLE copyright (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `cid` Nullable(Int64)\n);\nCREATE TABLE directed_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `did` Nullable(Int64)\n);\nCREATE TABLE director (\n `did` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `director_description` Nullable(String)\n);\nCREATE TABLE genre (\n `gid` Nullable(Int64),\n `genre` Nullable(String)\n);\nCREATE TABLE keyword (\n `id` Nullable(Int64),\n `keyword` Nullable(String)\n);\nCREATE TABLE made_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `pid` Nullable(Int64)\n);\nCREATE TABLE movie (\n `mid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `movie_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE producer (\n `pid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `producer_description` Nullable(String)\n);\nCREATE TABLE tags (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `kid` Nullable(Int64)\n);\nCREATE TABLE tv_series (\n `sid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `num_of_seasons` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `tv_series_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE writer (\n `wid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(Int64),\n `nationality` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `writer_description` Nullable(String)\n);\nCREATE TABLE written_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `wid` Nullable(Int64)\n);" + }, + { + "db_id": "e_government", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Contact details of John Doe who resides at 123 Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance\nFROM Individuals\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you identify the individual associated with the contact information for someone like John Doe living on Elm Street?", + "external_knowledge": "The `lembed` function generates an embedding vector from textual input using the `'all-MiniLM-L6-v2'` model. The `MATCH` operator conducts an approximate nearest neighbor search, which retrieves items based on vector similarity, typically using Euclidean distance. The similarity increases as the distance between vectors decreases. In this context, the operation aims to find individuals whose descriptions semantically relate to the idea of \"Contact details of John Doe who resides at 123 Elm Street,\" with the search constrained to return only one result.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the person linked to John Doe''''s contact info, who lives on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify the person associated with the contact details of John Doe on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Locate the individual connected to John Doe''''s contact information, residing on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for the person related to the contact info of John Doe who lives on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Discover the individual tied to John Doe''''s contact details, living at Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `town_city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Forms (\n `form_id` Nullable(Int64),\n `form_type_code` Nullable(String),\n `service_id` Nullable(Int64),\n `form_number` Nullable(String),\n `form_name` Nullable(String),\n `form_description` Nullable(String),\n `form_description_embedding` Array(Float32)\n);\nCREATE TABLE Individuals (\n `individual_id` Nullable(Int64),\n `individual_first_name` Nullable(String),\n `individual_middle_name` Nullable(String),\n `inidividual_phone` Nullable(String),\n `individual_email` Nullable(String),\n `individual_address` Nullable(String),\n `individual_last_name` Nullable(String),\n `Individuals_description` Nullable(String),\n `Individuals_description_embedding` Array(Float32)\n);\nCREATE TABLE Organization_Contact_Individuals (\n `individual_id` Int64,\n `organization_id` Int64,\n `date_contact_from` Date,\n `date_contact_to` Nullable(Date)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `date_formed` Nullable(String),\n `organization_name` Nullable(String),\n `uk_vat_number` Nullable(String),\n `Organizations_description` Nullable(String),\n `Organizations_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties (\n `party_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `party_phone` Nullable(String),\n `party_email` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Party_Addresses (\n `party_id` Int64,\n `address_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Party_Forms (\n `party_id` Int64,\n `form_id` Int64,\n `date_completion_started` Date,\n `form_status_code` String,\n `date_fully_completed` Nullable(Date)\n);\nCREATE TABLE Party_Services (\n `booking_id` Int64,\n `customer_id` Int64,\n `service_id` Int64,\n `service_datetime` Date,\n `booking_made_date` Nullable(Date)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `service_type_code` String,\n `service_name` Nullable(String),\n `service_descriptio` Nullable(String)\n);" + }, + { + "db_id": "imdb", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Great Adventure') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance\nFROM movie\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you fetch me the top 5 movies that have titles similar to \"The Great Adventure\"? I'd love to know their IDs and how close they are in terms of theme!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Epic Journey') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Grand Expedition') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Majestic Quest') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Incredible Voyage') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Great Exploration') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `aid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `actor_description` Nullable(String)\n);\nCREATE TABLE cast (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `aid` Nullable(Int64),\n `role` Nullable(Int64)\n);\nCREATE TABLE classification (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `gid` Nullable(Int64)\n);\nCREATE TABLE company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `country_code` Nullable(String),\n `company_description` Nullable(String)\n);\nCREATE TABLE copyright (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `cid` Nullable(Int64)\n);\nCREATE TABLE directed_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `did` Nullable(Int64)\n);\nCREATE TABLE director (\n `did` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `director_description` Nullable(String)\n);\nCREATE TABLE genre (\n `gid` Nullable(Int64),\n `genre` Nullable(String)\n);\nCREATE TABLE keyword (\n `id` Nullable(Int64),\n `keyword` Nullable(String)\n);\nCREATE TABLE made_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `pid` Nullable(Int64)\n);\nCREATE TABLE movie (\n `mid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `movie_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE producer (\n `pid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `producer_description` Nullable(String)\n);\nCREATE TABLE tags (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `kid` Nullable(Int64)\n);\nCREATE TABLE tv_series (\n `sid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `num_of_seasons` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `tv_series_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE writer (\n `wid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(Int64),\n `nationality` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `writer_description` Nullable(String)\n);\nCREATE TABLE written_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `wid` Nullable(Int64)\n);" + }, + { + "db_id": "tracking_software_problems", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'User Interface issues and errors') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance\nFROM Problem_Category_Codes pcc\nJOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 15, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the descriptions of the top 3 problem categories related to user interface issues and errors along with their corresponding log entry descriptions?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'UI problems and errors') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'interface issues and error messages') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'user interface related problems') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'UI issues and error logs') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'problems with user interface and errors') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "product_catalog", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3: ''''Tea Leaves'''' published by Green Tea Co. on March 15, 2015, last revised on September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance\nFROM Catalogs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the catalog name that best matches the description: \"Catalog ID 3: 'Tea Leaves' published by Green Tea Co. on March 15, 2015, last revised on September 12, 2019.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3 titled Tea Leaves by Green Tea Co., published on March 15, 2015, revised September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3: Tea Leaves, Green Tea Co. publisher, published March 15, 2015, last revised September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tea Leaves catalog by Green Tea Co., issued March 15, 2015, updated on September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3, Tea Leaves from Green Tea Co., March 15, 2015 publication, revised September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tea Leaves catalog from Green Tea Co., published March 2015, last updated September 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "pilot_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Experienced pilot from the USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom model equipped with diesel propulsion') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality\n FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain\n FROM aircraft_filtered AS aircraft\n)\n\nSELECT \n p.Pilot_name AS Pilot_name,\n p.Nationality AS Nationality,\n a.Manufacturer AS Manufacturer,\n a.Model AS Model,\n a.Powertrain AS Powertrain\nFROM FilteredPilots p\nJOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\nJOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID)\nORDER BY pr.Date DESC\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the names and nationalities of the top 3 experienced pilots from the USA and the manufacturers, models, and powertrains of the top 3 Gillig Phantom model aircraft equipped with diesel propulsion. Provide details for the most recent 5 pilot-aircraft pairings.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top skilled US pilots') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom diesel engine aircraft') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Veteran pilots from USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom with diesel propulsion system') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Experienced American pilots') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Diesel-powered Gillig Phantom aircraft') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly ranked pilots from USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom aircraft with diesel engines') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'USA pilots with extensive experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom model with diesel power') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Order_Year` Nullable(Int64),\n `Manufacturer` Nullable(String),\n `Model` Nullable(String),\n `Fleet_Series` Nullable(String),\n `Powertrain` Nullable(String),\n `Fuel_Propulsion` Nullable(String),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot (\n `Pilot_ID` Nullable(Int64),\n `Pilot_name` Nullable(String),\n `Rank` Nullable(Int64),\n `Age` Nullable(Int64),\n `Nationality` Nullable(String),\n `Position` Nullable(String),\n `Join_Year` Nullable(Int64),\n `Team` Nullable(String),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_record (\n `Record_ID` Nullable(Int64),\n `Pilot_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64),\n `Date` Nullable(String)\n);" + }, + { + "db_id": "musical", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A captivating musical journey with inspiring themes of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance\nFROM musical\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Can you identify the musical that most closely aligns with the themes of hope and redemption, described as a captivating journey?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A musical journey filled with themes of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An inspiring musical that explores hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A musical tale of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A journey through themes of hope and redemption in a musical') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A musical story highlighting themes of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `Actor_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Musical_ID` Nullable(Int64),\n `Character` Nullable(String),\n `Duration` Nullable(String),\n `age` Nullable(Int64),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE actor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE musical (\n `Musical_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Award` Nullable(String),\n `Category` Nullable(String),\n `Nominee` Nullable(String),\n `Result` Nullable(String),\n `musical_description` Nullable(String),\n `Category_embedding` Array(Float32),\n `musical_description_embedding` Array(Float32)\n);\nCREATE TABLE musical_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE musical_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "imdb", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An epic adventure in space') AS ref_vec_0\n\nSELECT m.title, d.name, distance(m.title_embedding, ref_vec_0) AS distance\nFROM movie m\nJOIN directed_by db ON toString(m.mid) = toString(db.msid)\nJOIN director d ON toString(db.did) = toString(d.did)\nJOIN classification cl ON toString(m.mid) = toString(cl.msid)\nJOIN genre g ON toString(cl.gid) = toString(g.gid)\nWHERE g.genre = 'Science Fiction'\nAND d.nationality = 'American'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find the top 5 American-directed science fiction movies that resonate with the idea of an epic space journey and tell me their titles and director names?", + "external_knowledge": "The `MATCH` operator in this query performs an approximate nearest neighbor search, which is used to find items that are most similar to a given vector representation, in this case, the concept of \"An epic adventure in space\". The `lembed` function generates embeddings for movie titles using a specified model, here 'all-MiniLM-L6-v2'. The `k = 5` clause limits the search to the top 5 most similar items based on vector similarity, where similarity is measured by the Euclidean distance. A smaller distance indicates a higher similarity to the concept. This approach is beneficial for identifying items that may not explicitly mention the concept but are semantically similar.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A grand space odyssey') AS ref_vec_0\n\nSELECT m.title, d.name, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN directed_by db ON toString(m.mid) = toString(db.msid) JOIN director d ON toString(db.did) = toString(d.did) JOIN classification cl ON toString(m.mid) = toString(cl.msid) JOIN genre g ON toString(cl.gid) = toString(g.gid) WHERE g.genre = 'Science Fiction' AND d.nationality = 'American'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Journey through the cosmos') AS ref_vec_0\n\nSELECT m.title, d.name, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN directed_by db ON toString(m.mid) = toString(db.msid) JOIN director d ON toString(db.did) = toString(d.did) JOIN classification cl ON toString(m.mid) = toString(cl.msid) JOIN genre g ON toString(cl.gid) = toString(g.gid) WHERE g.genre = 'Science Fiction' AND d.nationality = 'American'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Epic interstellar voyage') AS ref_vec_0\n\nSELECT m.title, d.name, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN directed_by db ON toString(m.mid) = toString(db.msid) JOIN director d ON toString(db.did) = toString(d.did) JOIN classification cl ON toString(m.mid) = toString(cl.msid) JOIN genre g ON toString(cl.gid) = toString(g.gid) WHERE g.genre = 'Science Fiction' AND d.nationality = 'American'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Space exploration epic') AS ref_vec_0\n\nSELECT m.title, d.name, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN directed_by db ON toString(m.mid) = toString(db.msid) JOIN director d ON toString(db.did) = toString(d.did) JOIN classification cl ON toString(m.mid) = toString(cl.msid) JOIN genre g ON toString(cl.gid) = toString(g.gid) WHERE g.genre = 'Science Fiction' AND d.nationality = 'American'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cosmic adventure saga') AS ref_vec_0\n\nSELECT m.title, d.name, distance(m.title_embedding, ref_vec_0) AS distance FROM movie m JOIN directed_by db ON toString(m.mid) = toString(db.msid) JOIN director d ON toString(db.did) = toString(d.did) JOIN classification cl ON toString(m.mid) = toString(cl.msid) JOIN genre g ON toString(cl.gid) = toString(g.gid) WHERE g.genre = 'Science Fiction' AND d.nationality = 'American'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'title_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `aid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `actor_description` Nullable(String)\n);\nCREATE TABLE cast (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `aid` Nullable(Int64),\n `role` Nullable(Int64)\n);\nCREATE TABLE classification (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `gid` Nullable(Int64)\n);\nCREATE TABLE company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `country_code` Nullable(String),\n `company_description` Nullable(String)\n);\nCREATE TABLE copyright (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `cid` Nullable(Int64)\n);\nCREATE TABLE directed_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `did` Nullable(Int64)\n);\nCREATE TABLE director (\n `did` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `director_description` Nullable(String)\n);\nCREATE TABLE genre (\n `gid` Nullable(Int64),\n `genre` Nullable(String)\n);\nCREATE TABLE keyword (\n `id` Nullable(Int64),\n `keyword` Nullable(String)\n);\nCREATE TABLE made_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `pid` Nullable(Int64)\n);\nCREATE TABLE movie (\n `mid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `movie_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE producer (\n `pid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `producer_description` Nullable(String)\n);\nCREATE TABLE tags (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `kid` Nullable(Int64)\n);\nCREATE TABLE tv_series (\n `sid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `num_of_seasons` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `tv_series_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE writer (\n `wid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(Int64),\n `nationality` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `writer_description` Nullable(String)\n);\nCREATE TABLE written_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `wid` Nullable(Int64)\n);" + }, + { + "db_id": "pilot_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'experienced pilot from the US in a leadership role') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot p\n JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer\nFROM FilteredPilots fp\nJOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID)\nORDER BY fp.distance LIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the names and associated aircraft manufacturers of the top two pilots who best fit the profile of an experienced pilot from the US holding a leadership role.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'veteran US pilot with leadership experience') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'seasoned American pilot in a senior position') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'experienced US pilot with managerial duties') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'American pilot with extensive experience and leadership role') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'US pilot with significant experience in leadership') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Order_Year` Nullable(Int64),\n `Manufacturer` Nullable(String),\n `Model` Nullable(String),\n `Fleet_Series` Nullable(String),\n `Powertrain` Nullable(String),\n `Fuel_Propulsion` Nullable(String),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot (\n `Pilot_ID` Nullable(Int64),\n `Pilot_name` Nullable(String),\n `Rank` Nullable(Int64),\n `Age` Nullable(Int64),\n `Nationality` Nullable(String),\n `Position` Nullable(String),\n `Join_Year` Nullable(Int64),\n `Team` Nullable(String),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_record (\n `Record_ID` Nullable(Int64),\n `Pilot_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64),\n `Date` Nullable(String)\n);" + }, + { + "db_id": "e_government", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Central Park in New York City, known for its vast green spaces, located in the USA') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance \nFROM Addresses\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the address ID for the location most similar to Central Park in New York City.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Famous urban park in New York City, USA, known for its green areas and recreational spaces') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Large public park located in NYC, celebrated for its nature and open spaces') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Iconic park in Manhattan, New York, recognized for its expansive greenery and attractions') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Central Park, a notable green space in New York City, offering vast landscapes and leisure activities') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned park in NYC, USA, featuring extensive gardens and recreational areas') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `town_city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Forms (\n `form_id` Nullable(Int64),\n `form_type_code` Nullable(String),\n `service_id` Nullable(Int64),\n `form_number` Nullable(String),\n `form_name` Nullable(String),\n `form_description` Nullable(String),\n `form_description_embedding` Array(Float32)\n);\nCREATE TABLE Individuals (\n `individual_id` Nullable(Int64),\n `individual_first_name` Nullable(String),\n `individual_middle_name` Nullable(String),\n `inidividual_phone` Nullable(String),\n `individual_email` Nullable(String),\n `individual_address` Nullable(String),\n `individual_last_name` Nullable(String),\n `Individuals_description` Nullable(String),\n `Individuals_description_embedding` Array(Float32)\n);\nCREATE TABLE Organization_Contact_Individuals (\n `individual_id` Int64,\n `organization_id` Int64,\n `date_contact_from` Date,\n `date_contact_to` Nullable(Date)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `date_formed` Nullable(String),\n `organization_name` Nullable(String),\n `uk_vat_number` Nullable(String),\n `Organizations_description` Nullable(String),\n `Organizations_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties (\n `party_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `party_phone` Nullable(String),\n `party_email` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Party_Addresses (\n `party_id` Int64,\n `address_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Party_Forms (\n `party_id` Int64,\n `form_id` Int64,\n `date_completion_started` Date,\n `form_status_code` String,\n `date_fully_completed` Nullable(Date)\n);\nCREATE TABLE Party_Services (\n `booking_id` Int64,\n `customer_id` Int64,\n `service_id` Int64,\n `service_datetime` Date,\n `booking_made_date` Nullable(Date)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `service_type_code` String,\n `service_name` Nullable(String),\n `service_descriptio` Nullable(String)\n);" + }, + { + "db_id": "product_catalog", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high-quality chocolate bar with rich flavor and smooth texture.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT \n catalog_entry_id, \n price_in_dollars,\n distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance\n FROM \n Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n AVG(price_in_dollars) AS average_price_in_dollars\nFROM \n SimilarCatalogs;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "What is the average price of a selection of chocolate bars that are really similar to a top-notch one with a delightful taste and smooth feel?", + "external_knowledge": "In vector searches using the `sqlite-lembed` extension, the `MATCH` operator performs an approximate nearest neighbor (ANN) search to find items similar to a given query vector. The parameter `k=5` specifies that the query should return the top 5 items that are most similar to the query vector. This similarity is usually determined by calculating the Euclidean distance (L2 norm) between vectors, where a smaller distance indicates higher similarity. In this context, the query seeks to find chocolate bars that are most similar to the description of a \"high-quality chocolate bar with rich flavor and smooth texture.\" The description is converted into an embedding using the `lembed` function with the 'all-MiniLM-L6-v2' model, which represents semantic meanings in a high-dimensional space.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A premium chocolate bar with exquisite taste and velvety texture.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A luxurious chocolate bar known for its delightful taste and smooth finish.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An elite chocolate bar with a rich flavor profile and silky texture.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-tier chocolate bar characterized by its delightful taste and creamy feel.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A gourmet chocolate bar with a luscious flavor and smooth mouthfeel.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;" + ], + "integration_level": 4, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "shop_membership", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Branch located in London with historical significance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance\nFROM membership_register_branch mr\nJOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Please provide the IDs of members associated with the top 5 branches that are described as being located in London with historical significance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Branches in London with historical relevance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'London branches with historical importance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Historically significant branches in London') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Branches located in London known for historical significance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top branches in London with historical significance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE branch (\n `Branch_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Open_year` Nullable(String),\n `Address_road` Nullable(String),\n `City` Nullable(String),\n `membership_amount` Nullable(String),\n `branch_description` Nullable(String),\n `branch_description_embedding` Array(Float32)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Card_Number` Nullable(String),\n `Name` Nullable(String),\n `Hometown` Nullable(String),\n `Level` Nullable(Int64),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE membership_register_branch (\n `Member_ID` Nullable(Int64),\n `Branch_ID` Nullable(String),\n `Register_Year` Nullable(String)\n);\nCREATE TABLE purchase (\n `Member_ID` Nullable(Int64),\n `Branch_ID` Nullable(String),\n `Year` Nullable(String),\n `Total_pounds` Nullable(Float64)\n);" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John Doe, male, born on January 1, 1990, lives at 123 Main St, Anytown, USA, earns $50,000 annually, supervised by SSN 123456789, works in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance\nFROM employee\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the Social Security Number of the employee whose profile is most similar to that of John Doe, who is a male, born on January 1, 1990, currently residing at 123 Main St, Anytown, USA, earns $50,000 annually, is supervised by SSN 123456789, and works in department 3?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the employee with a profile closest to John Doe, a male born on January 1, 1990, residing at 123 Main St, Anytown, USA, earning $50,000 annually, supervised by SSN 123456789, and working in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for the SSN of the employee most similar to John Doe, who is a male, born on 1990-01-01, lives at 123 Main St, Anytown, USA, has a salary of $50,000, is overseen by SSN 123456789, and belongs to department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify the employee whose profile is most like John Doe''''s, a male born on January 1, 1990, currently living at 123 Main St, Anytown, USA, with an annual income of $50,000, under the supervision of SSN 123456789, and assigned to department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Retrieve the Social Security Number of the employee with the most similar profile to John Doe, male, born on January 1, 1990, residing at 123 Main St, Anytown, USA, earning $50,000 per year, supervised by SSN 123456789, and working in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Get the SSN of the employee whose profile matches John Doe''''s: male, born January 1, 1990, lives at 123 Main St, Anytown, USA, earns $50,000 annually, supervised by SSN 123456789, and works in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);" + }, + { + "db_id": "protein_institute", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard in London is a renowned skyscraper, known for its stunning glass facade and iconic silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance \nFROM building\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you find the building that is most representative of the description \"The Shard in London is a renowned skyscraper, known for its stunning glass facade and iconic silhouette,\" and give me its ID and the similarity distance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard is a famous skyscraper in London, celebrated for its glass facade and distinctive silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Known for its stunning glass exterior and iconic shape, The Shard in London stands out as a remarkable skyscraper.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard in London is a notable skyscraper, recognized for its beautiful glass facade and unique silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'London''''s Shard is a renowned skyscraper, distinguished by its impressive glass facade and iconic outline.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard, a prominent skyscraper in London, is known for its striking glass facade and memorable silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Institution (\n `Institution_id` Nullable(String),\n `Institution` Nullable(String),\n `Location` Nullable(String),\n `Founded` Nullable(Float64),\n `Type` Nullable(String),\n `Enrollment` Nullable(Int64),\n `Team` Nullable(String),\n `Primary_Conference` Nullable(String),\n `building_id` Nullable(String),\n `Institution_description` Nullable(String),\n `Institution_description_embedding` Array(Float32)\n);\nCREATE TABLE Institution_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE building (\n `building_id` Nullable(String),\n `Name` Nullable(String),\n `Street_address` Nullable(String),\n `Years_as_tallest` Nullable(String),\n `Height_feet` Nullable(Int64),\n `Floors` Nullable(Int64),\n `building_description` Nullable(String),\n `building_description_embedding` Array(Float32)\n);\nCREATE TABLE building_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE protein (\n `common_name` Nullable(String),\n `protein_name` Nullable(String),\n `divergence_from_human_lineage` Nullable(Float64),\n `accession_number` Nullable(String),\n `sequence_length` Nullable(Float64),\n `sequence_identity_to_human_protein` Nullable(String),\n `Institution_id` Nullable(String),\n `protein_description` Nullable(String),\n `protein_description_embedding` Array(Float32)\n);\nCREATE TABLE protein_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "voter_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A student from New York majoring in computer science') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT\n StuID,\n Election_Cycle,\n President_Vote,\n Vice_President_Vote\n FROM\n Voting_record\n WHERE\n Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT\n StuID,\n LName,\n Fname,\n Major,\n Advisor,\n distance(Student.Student_description_embedding, ref_vec_0) AS distance\n FROM\n Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT\n SS.StuID AS StuID\nFROM\n StudentSearch SS\nJOIN\n RecentVotes RV\nON toString(SS.StuID) = toString(RV.StuID)\nORDER BY\n SS.distance AS distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the top student who recently registered to vote after January 1, 2023, and is majoring in computer science from New York? I need their ID based on semantic similarity using the \"all-MiniLM-L6-v2\" model!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A computer science student from New York who registered to vote recently') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York student studying computer science registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Computer science major from New York who recently registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Student majoring in computer science from New York who recently registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York computer science student who has recently registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Voting_record (\n `StuID` Nullable(Int64),\n `Registration_Date` Nullable(String),\n `Election_Cycle` Nullable(String),\n `President_Vote` Nullable(Int64),\n `Vice_President_Vote` Nullable(Int64),\n `Secretary_Vote` Nullable(Int64),\n `Treasurer_Vote` Nullable(Int64),\n `Class_President_Vote` Nullable(Int64),\n `Class_Senator_Vote` Nullable(Int64)\n);" + }, + { + "db_id": "loan_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Home loan for customer with high credit score.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer with multiple loans and high balance.') AS ref_vec_1,\n\nloan_filtered AS (\n SELECT\n *,\n distance(loan_description_embedding, ref_vec_0) AS distance\n FROM loan\n\n ORDER BY distance\n LIMIT 5\n),\n\ncustomer_filtered AS (\n SELECT\n *,\n distance(customer_description_embedding, ref_vec_1) AS distance\n FROM customer\n WHERE customer_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Customer with multiple loans\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT \n loan_ID, \n loan_type,\n cust_ID, \n branch_ID, \n amount,\n distance AS loan_distance\n FROM loan_filtered AS loan\n),\n\nCTE_Customers AS (\n SELECT \n cust_ID, \n cust_name, \n acc_type, \n acc_bal, \n branch_ID,\n credit_score,\n distance AS customer_distance\n FROM customer_filtered AS customer high balance.')\n)\n\nSELECT \n C.cust_name AS cust_name\nFROM \n CTE_Loans L\nJOIN \n CTE_Customers C ON toString(L.cust_ID) = toString(C.cust_ID)\nWHERE \n C.credit_score > 700\nORDER BY \n C.customer_distance AS customer_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Can you tell me the name of the top customer with a high credit score who has taken a home loan and has multiple loans with a big account balance? I'm just curious who stands out the most!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Home loan for a top customer with excellent credit.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer with several loans and substantial account balance.') AS ref_vec_1,\n\nloan_filtered AS (\n SELECT\n *,\n distance(loan_description_embedding, ref_vec_0) AS distance\n FROM loan\n\n ORDER BY distance\n LIMIT 5\n),\n\ncustomer_filtered AS (\n SELECT\n *,\n distance(customer_description_embedding, ref_vec_1) AS distance\n FROM customer\n WHERE customer_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Customer with several loans\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT loan_ID, loan_type, cust_ID, branch_ID, amount, distance AS loan_distance FROM loan_filtered AS loan\n),\n\nCTE_Customers AS (\n SELECT cust_ID, cust_name, acc_type, acc_bal, branch_ID, credit_score, distance AS customer_distance FROM customer_filtered AS customer substantial account balance.')\n)\n\nSELECT C.cust_name FROM CTE_Loans L JOIN CTE_Customers C ON toString(L.cust_ID) = toString(C.cust_ID) WHERE C.credit_score > 700 ORDER BY C.customer_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High credit score customer with a home loan.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer with multiple financial obligations and large balance.') AS ref_vec_1,\n\nloan_filtered AS (\n SELECT\n *,\n distance(loan_description_embedding, ref_vec_0) AS distance\n FROM loan\n\n ORDER BY distance\n LIMIT 5\n),\n\ncustomer_filtered AS (\n SELECT\n *,\n distance(customer_description_embedding, ref_vec_1) AS distance\n FROM customer\n WHERE customer_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Customer with multiple financial obligations\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT loan_ID, loan_type, cust_ID, branch_ID, amount, distance AS loan_distance FROM loan_filtered AS loan\n),\n\nCTE_Customers AS (\n SELECT cust_ID, cust_name, acc_type, acc_bal, branch_ID, credit_score, distance AS customer_distance FROM customer_filtered AS customer large balance.')\n)\n\nSELECT C.cust_name FROM CTE_Loans L JOIN CTE_Customers C ON toString(L.cust_ID) = toString(C.cust_ID) WHERE C.credit_score > 700 ORDER BY C.customer_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Home mortgage for a customer with high credit.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer with various loans and significant bank balance.') AS ref_vec_1,\n\nloan_filtered AS (\n SELECT\n *,\n distance(loan_description_embedding, ref_vec_0) AS distance\n FROM loan\n\n ORDER BY distance\n LIMIT 5\n),\n\ncustomer_filtered AS (\n SELECT\n *,\n distance(customer_description_embedding, ref_vec_1) AS distance\n FROM customer\n WHERE customer_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Customer with various loans\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT loan_ID, loan_type, cust_ID, branch_ID, amount, distance AS loan_distance FROM loan_filtered AS loan\n),\n\nCTE_Customers AS (\n SELECT cust_ID, cust_name, acc_type, acc_bal, branch_ID, credit_score, distance AS customer_distance FROM customer_filtered AS customer significant bank balance.')\n)\n\nSELECT C.cust_name FROM CTE_Loans L JOIN CTE_Customers C ON toString(L.cust_ID) = toString(C.cust_ID) WHERE C.credit_score > 700 ORDER BY C.customer_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top customer with a home loan and excellent credit score.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer managing multiple loans with a large account balance.') AS ref_vec_1,\n\nloan_filtered AS (\n SELECT\n *,\n distance(loan_description_embedding, ref_vec_0) AS distance\n FROM loan\n WHERE loan_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Top customer with a home loan\n ORDER BY distance\n LIMIT 5\n),\n\ncustomer_filtered AS (\n SELECT\n *,\n distance(customer_description_embedding, ref_vec_1) AS distance\n FROM customer\n\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT loan_ID, loan_type, cust_ID, branch_ID, amount, distance AS loan_distance FROM loan_filtered AS loan excellent credit score.')\n),\n\nCTE_Customers AS (\n SELECT cust_ID, cust_name, acc_type, acc_bal, branch_ID, credit_score, distance AS customer_distance FROM customer_filtered AS customer\n)\n\nSELECT C.cust_name FROM CTE_Loans L JOIN CTE_Customers C ON toString(L.cust_ID) = toString(C.cust_ID) WHERE C.credit_score > 700 ORDER BY C.customer_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Home loan for a high credit score individual.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer with numerous loans and a high account balance.') AS ref_vec_1,\n\nloan_filtered AS (\n SELECT\n *,\n distance(loan_description_embedding, ref_vec_0) AS distance\n FROM loan\n\n ORDER BY distance\n LIMIT 5\n),\n\ncustomer_filtered AS (\n SELECT\n *,\n distance(customer_description_embedding, ref_vec_1) AS distance\n FROM customer\n WHERE customer_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Customer with numerous loans\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT loan_ID, loan_type, cust_ID, branch_ID, amount, distance AS loan_distance FROM loan_filtered AS loan\n),\n\nCTE_Customers AS (\n SELECT cust_ID, cust_name, acc_type, acc_bal, branch_ID, credit_score, distance AS customer_distance FROM customer_filtered AS customer a high account balance.')\n)\n\nSELECT C.cust_name FROM CTE_Loans L JOIN CTE_Customers C ON toString(L.cust_ID) = toString(C.cust_ID) WHERE C.credit_score > 700 ORDER BY C.customer_distance LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17245 ('MATCH') (line 20, col 42): MATCH lembed('all-MiniLM-L6-v2', 'Customer with multiple loans\n ORDER BY distance\n LIMIT 5\n),\n\nCTE_Loans AS (\n SELECT \n loan_ID, \n loan_t. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE bank (\n `branch_ID` Nullable(Int64),\n `bname` Nullable(String),\n `no_of_customers` Nullable(Int64),\n `city` Nullable(String),\n `state` Nullable(String),\n `bank_description` Nullable(String),\n `bank_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `cust_ID` Nullable(String),\n `cust_name` Nullable(String),\n `acc_type` Nullable(String),\n `acc_bal` Nullable(Int64),\n `no_of_loans` Nullable(Int64),\n `credit_score` Nullable(Int64),\n `branch_ID` Nullable(Int64),\n `state` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE loan (\n `loan_ID` Nullable(String),\n `loan_type` Nullable(String),\n `cust_ID` Nullable(String),\n `branch_ID` Nullable(String),\n `amount` Nullable(Int64),\n `loan_description` Nullable(String),\n `loan_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A major infrastructure project located at Silicon Valley, managed by the IT department.') AS ref_vec_0\n\nSELECT e.Fname || ' ' || e.Lname AS Employee_Name, \n SUM(w.Hours) AS Total_Hours_Worked, distance(p.project_description_embedding, ref_vec_0) AS distance\nFROM works_on w\nJOIN (\n SELECT p.Pnumber, p.Dnum, distance \n FROM project p\n \n \n) AS top_projects ON toString(w.Pno) = toString(top_projects.Pnumber)\nJOIN employee e ON toString(w.Essn) = toString(e.Ssn)\nJOIN department d ON toString(top_projects.Dnum) = toString(d.Dnumber)\nWHERE d.Dname = 'IT Department'\nGROUP BY e.Ssn\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you tell me who are the top 5 employees from the IT Department that have worked the most hours on projects related to a major infrastructure initiative in Silicon Valley? Please provide their full names and the total hours they worked.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Key infrastructure initiative in Silicon Valley by IT team') AS ref_vec_0\n\nSELECT e.Fname || ' ' || e.Lname AS Employee_Name, SUM(w.Hours) AS Total_Hours_Worked, distance(p.project_description_embedding, ref_vec_0) AS distance FROM works_on w JOIN ( SELECT p.Pnumber, p.Dnum, distance FROM project p ) AS top_projects ON toString(w.Pno) = toString(top_projects.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) JOIN department d ON toString(top_projects.Dnum) = toString(d.Dnumber) WHERE d.Dname = 'IT Department' GROUP BY e.Ssn\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'IT department-led major infrastructure project in Silicon Valley') AS ref_vec_0\n\nSELECT e.Fname || ' ' || e.Lname AS Employee_Name, SUM(w.Hours) AS Total_Hours_Worked, distance(p.project_description_embedding, ref_vec_0) AS distance FROM works_on w JOIN ( SELECT p.Pnumber, p.Dnum, distance FROM project p ) AS top_projects ON toString(w.Pno) = toString(top_projects.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) JOIN department d ON toString(top_projects.Dnum) = toString(d.Dnumber) WHERE d.Dname = 'IT Department' GROUP BY e.Ssn\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Silicon Valley infrastructure initiative managed by IT department') AS ref_vec_0\n\nSELECT e.Fname || ' ' || e.Lname AS Employee_Name, SUM(w.Hours) AS Total_Hours_Worked, distance(p.project_description_embedding, ref_vec_0) AS distance FROM works_on w JOIN ( SELECT p.Pnumber, p.Dnum, distance FROM project p ) AS top_projects ON toString(w.Pno) = toString(top_projects.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) JOIN department d ON toString(top_projects.Dnum) = toString(d.Dnumber) WHERE d.Dname = 'IT Department' GROUP BY e.Ssn\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'IT department infrastructure project in Silicon Valley') AS ref_vec_0\n\nSELECT e.Fname || ' ' || e.Lname AS Employee_Name, SUM(w.Hours) AS Total_Hours_Worked, distance(p.project_description_embedding, ref_vec_0) AS distance FROM works_on w JOIN ( SELECT p.Pnumber, p.Dnum, distance FROM project p ) AS top_projects ON toString(w.Pno) = toString(top_projects.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) JOIN department d ON toString(top_projects.Dnum) = toString(d.Dnumber) WHERE d.Dname = 'IT Department' GROUP BY e.Ssn\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Major IT-led infrastructure project in Silicon Valley') AS ref_vec_0\n\nSELECT e.Fname || ' ' || e.Lname AS Employee_Name, SUM(w.Hours) AS Total_Hours_Worked, distance(p.project_description_embedding, ref_vec_0) AS distance FROM works_on w JOIN ( SELECT p.Pnumber, p.Dnum, distance FROM project p ) AS top_projects ON toString(w.Pno) = toString(top_projects.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) JOIN department d ON toString(top_projects.Dnum) = toString(d.Dnumber) WHERE d.Dname = 'IT Department' GROUP BY e.Ssn\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'distance' while processing query: 'WITH [0.017807701602578163, 0.018830081447958946, 0.03916145861148834, -0.005434294231235981, 0.03506007418036461, -0.07184188067913055, -0.029122337698936462, -0.04583527892827988, -0.041259344667196274, 0.0048424615524709225, -0.022029567509889603, 0.03475455567240715, 0.050502579659223557, -0.004356762859970331, -0.04610663652420044, 0.0098162442445755, 0.004405635874718428, -0.08334221690893173, 0.01935734413564205, -0.0732099860906601, 0.06565242260694504, 0.0016096296021714807, -0.015739308670163155, -0.0726495161652565, 0.002488820580765605, 0.023453345522284508, -0.009816491976380348, -0.00965227372944355, -0.07029589265584946, -0.030029483139514923, -0.04860542342066765, 0.044732704758644104, 0.04859226197004318, -0.019134698435664177, 0.05002901703119278, 0.10001818090677261, 0.04575267806649208, -0.0064829085022211075, 0.022863181307911873, -0.07676400989294052, -0.029207291081547737, -0.07497120648622513, 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0.06185483559966087, -0.0031195294577628374, -0.007858436554670334, 0.09814468026161194, -0.1250128149986267, -0.02362941950559616, -0.010074527002871037, 0.047463126480579376, 0.013432563282549381, 0.059651900082826614, 0.09179205447435379, -0.11598533391952515, -0.03132347762584686, 0.01352275162935257, 0.04226437211036682, -0.0675496831536293, -0.03508609160780907, -0.02475079521536827, 0.007284190971404314, -0.030924418941140175, 0.1631280481815338, -0.004645583685487509, 0.022979507222771645, 0.07698444277048111, 0.04700217768549919, 0.019609758630394936, -0.01958427019417286, 0.04189314320683479, 0.013941560871899128, -0.06472842395305634, 0.023608865216374397, 0.02344970777630806, -0.08740037679672241, -0.032474078238010406, -0.006367585156112909, 0.06292370706796646, 0.04025958850979805, 0.07881657779216766, 0.006619774736464024, 0.0002452244807500392, 0.005569844506680965, -0.012399164959788322, 0.03882845863699913, -0.020446086302399635, 0.039391495287418365, 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1.710725397755484e-33, -0.05860061198472977, 0.004270255099982023, -0.020860224962234497, 0.02832587994635105, 0.018373213708400726, -0.02330014295876026, 0.016209779307246208, -0.06841520965099335, -0.0027244051452726126, 0.08695679157972336, -0.045085810124874115, 0.0103785190731287, 0.03217102587223053, -0.011010968126356602, 0.05073288455605507, 0.06050540506839752, 0.06510509550571442, -0.10300500690937042, -0.04898457229137421, 0.032615020871162415, -0.01689302548766136, 0.016195446252822876, -0.01214572787284851, -0.060074470937252045, 0.015916677191853523, 0.005670099053531885, -0.022937864065170288, -0.08490647375583649, 0.024997925385832787, 0.037049539387226105, -0.06838616728782654, -0.10452219098806381, -0.04570232331752777, 0.011110803112387657, -0.013564422726631165, 0.048306699842214584, 0.019722655415534973, -0.08976589143276215, 0.0022002779878675938, -0.03219134733080864, 0.08863163739442825, -0.04735404998064041, -0.05673401802778244, 0.03658001124858856, 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0.14680154621601105, -0.030040321871638298, -0.044035911560058594, 0.03196754306554794, -0.012218648567795753, 0.0834299698472023, 0.03679059073328972, 0.024318140000104904, -0.07919703423976898, -0.028835998848080635, 0.001873952685855329, 0.00634038494899869, -0.04917106404900551, 0.08947780728340149, 0.006470926571637392, 0.10163678228855133, -0.012896057218313217, 0.010199163109064102, 0.02718385122716427, -0.10352732986211777, -0.02080240473151207, 0.09973354637622833, 0.011236018501222134, 0.004863651469349861, 0.04261412471532822, -0.013777491636574268] AS ref_vec_0 SELECT Pnumber, Dnum, distance FROM project AS p', required columns: 'Pnumber' 'Dnum' 'distance', maybe you meant: 'Pnumber' or 'Dnum'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative technology development') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT \n Pnumber, \n Pname, \n distance(project.project_description_embedding, ref_vec_0) AS distance\n FROM \n project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n e.Fname || ' ' || e.Lname AS EmployeeName,\n p.Pname AS ProjectName\nFROM \n works_on w\nJOIN \n ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber)\nJOIN \n employee e ON toString(w.Essn) = toString(e.Ssn)\nORDER BY \n p.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Could you provide the names of employees and the projects they are currently working on, specifically for the top 5 projects that are most related to \"Innovative technology development\"? Please ensure that the results are ordered by their similarity distance and limited to the top 10 entries.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge tech development') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced technology innovation') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pioneering tech projects') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative R&D in technology') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tech innovation and development') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);" + }, + { + "db_id": "debate", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A representative from California District 5 who is a Democrat aged 40.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance\nFROM people\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the grand theater of democracy, who are the five performers that step into the shoes of a 40-year-old Democrat representing California District 5?", + "external_knowledge": "The `MATCH` operator in the context of vector operations performs an approximate nearest neighbor (ANN) search, which is a common technique used to find data points that are most similar to a given query vector. The `lembed` function processes the input text \"A representative from California District 5 who is a Democrat aged 40\" using the 'all-MiniLM-L6-v2' model to generate a vector representation. The \"k=5\" clause specifies that the query should return the five nearest matches based on Euclidean distance. Lower distances indicate higher similarity to the provided description.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A 40-year-old Democrat from California''''s 5th district.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'California District 5 Democrat, aged 40.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Democratic representative, age 40, from California District 5.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Aged 40, Democrat, representing California''''s 5th District.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '40-year-old Democrat in California''''s 5th district.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE debate (\n `Debate_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Num_of_Audience` Nullable(Int64),\n `debate_description` Nullable(String),\n `debate_description_embedding` Array(Float32)\n);\nCREATE TABLE debate_people (\n `Debate_ID` Nullable(Int64),\n `Affirmative` Nullable(Int64),\n `Negative` Nullable(Int64),\n `If_Affirmative_Win` Nullable(String)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `District` Nullable(String),\n `Name` Nullable(String),\n `Party` Nullable(String),\n `Age` Nullable(Int64),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Marketing department, numbered 3, is managed by an individual with SSN 123456789, who began managing the department on February 10, 2010.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance\nFROM department\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the top 5 departments that are most relevant to a description about the Marketing department, including the department name and similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Explore the top 5 departments closely related to the Marketing division, focusing on department names and similarity measures.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the five departments most similar to the Marketing department, including their names and similarity scores.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify departments most aligned with Marketing, showing department names and similarity levels.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Determine which five departments are most associated with Marketing, including their names and similarity distances.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'List the top five departments that have the strongest connection to Marketing, with department names and similarity metrics.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);" + }, + { + "db_id": "assets_maintenance", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Tech and consulting company specialized in AI') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Advanced AI-based software solutions') AS ref_vec_1,\n\nThird_Party_Companies_filtered AS (\n SELECT\n *,\n distance(Third_Party_Companies_description_embedding, ref_vec_0) AS distance\n FROM Third_Party_Companies\n WHERE Third_Party_Companies_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Tech AND consulting company specialized in AI')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(Assets_description_embedding, ref_vec_1) AS distance\n FROM Assets\n\n ORDER BY distance\n LIMIT 5\n),\n\nFiltered_Companies AS (\n SELECT company_id, company_name, distance AS company_distance\n FROM Third_Party_Companies_filtered AS Third_Party_Companies\n)\n\nSELECT c.company_name\nFROM Filtered_Companies c\nJOIN a_filtered AS a ON toString(c.company_id) = toString(a.supplier_company_id)\nORDER BY a.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the names of the top 10 companies that are known for tech and consulting in AI and supply assets related to advanced AI-based software solutions?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Companies known for AI tech and consulting services') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'AI-driven software asset providers') AS ref_vec_1,\n\nThird_Party_Companies_filtered AS (\n SELECT\n *,\n distance(Third_Party_Companies_description_embedding, ref_vec_0) AS distance\n FROM Third_Party_Companies\n WHERE Third_Party_Companies_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Companies known for AI tech AND consulting services')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(Assets_description_embedding, ref_vec_1) AS distance\n FROM Assets\n\n ORDER BY distance\n LIMIT 5\n),\n\nFiltered_Companies AS (\n SELECT company_id, company_name, distance AS company_distance FROM Third_Party_Companies_filtered AS Third_Party_Companies\n)\n\nSELECT c.company_name FROM Filtered_Companies c JOIN a_filtered AS a ON toString(c.company_id) = toString(a.supplier_company_id) ORDER BY a.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading firms in AI technology and consulting') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Suppliers of advanced AI software solutions') AS ref_vec_1,\n\nThird_Party_Companies_filtered AS (\n SELECT\n *,\n distance(Third_Party_Companies_description_embedding, ref_vec_0) AS distance\n FROM Third_Party_Companies\n WHERE Third_Party_Companies_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Leading firms in AI technology AND consulting')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(Assets_description_embedding, ref_vec_1) AS distance\n FROM Assets\n\n ORDER BY distance\n LIMIT 5\n),\n\nFiltered_Companies AS (\n SELECT company_id, company_name, distance AS company_distance FROM Third_Party_Companies_filtered AS Third_Party_Companies\n)\n\nSELECT c.company_name FROM Filtered_Companies c JOIN a_filtered AS a ON toString(c.company_id) = toString(a.supplier_company_id) ORDER BY a.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top AI consulting and tech firms') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Providers of AI-based software assets') AS ref_vec_1,\n\nThird_Party_Companies_filtered AS (\n SELECT\n *,\n distance(Third_Party_Companies_description_embedding, ref_vec_0) AS distance\n FROM Third_Party_Companies\n WHERE Third_Party_Companies_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Top AI consulting AND tech firms')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(Assets_description_embedding, ref_vec_1) AS distance\n FROM Assets\n\n ORDER BY distance\n LIMIT 5\n),\n\nFiltered_Companies AS (\n SELECT company_id, company_name, distance AS company_distance FROM Third_Party_Companies_filtered AS Third_Party_Companies\n)\n\nSELECT c.company_name FROM Filtered_Companies c JOIN a_filtered AS a ON toString(c.company_id) = toString(a.supplier_company_id) ORDER BY a.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'AI-focused tech and consulting businesses') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Advanced AI software solution suppliers') AS ref_vec_1,\n\nThird_Party_Companies_filtered AS (\n SELECT\n *,\n distance(Third_Party_Companies_description_embedding, ref_vec_0) AS distance\n FROM Third_Party_Companies\n WHERE Third_Party_Companies_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'AI-focused tech AND consulting businesses')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(Assets_description_embedding, ref_vec_1) AS distance\n FROM Assets\n\n ORDER BY distance\n LIMIT 5\n),\n\nFiltered_Companies AS (\n SELECT company_id, company_name, distance AS company_distance FROM Third_Party_Companies_filtered AS Third_Party_Companies\n)\n\nSELECT c.company_name FROM Filtered_Companies c JOIN a_filtered AS a ON toString(c.company_id) = toString(a.supplier_company_id) ORDER BY a.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Consulting and tech leaders in AI') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'AI software solution providers') AS ref_vec_1,\n\nThird_Party_Companies_filtered AS (\n SELECT\n *,\n distance(Third_Party_Companies_description_embedding, ref_vec_0) AS distance\n FROM Third_Party_Companies\n WHERE Third_Party_Companies_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Consulting AND tech leaders in AI')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(Assets_description_embedding, ref_vec_1) AS distance\n FROM Assets\n\n ORDER BY distance\n LIMIT 5\n),\n\nFiltered_Companies AS (\n SELECT company_id, company_name, distance AS company_distance FROM Third_Party_Companies_filtered AS Third_Party_Companies\n)\n\nSELECT c.company_name FROM Filtered_Companies c JOIN a_filtered AS a ON toString(c.company_id) = toString(a.supplier_company_id) ORDER BY a.distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17132 ('MATCH') (line 10, col 55): MATCH [-0.04674302786588669, -0.05163370072841644, -0.04542149230837822, 0.0006843514274805784, -0.05698473006486893, -0.0372329019010067, 0.078827403485775, 0.. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Asset_Parts (\n `asset_id` Int64,\n `part_id` Int64\n);\nCREATE TABLE Assets (\n `asset_id` Nullable(Int64),\n `maintenance_contract_id` Nullable(Int64),\n `supplier_company_id` Nullable(Int64),\n `asset_details` Nullable(String),\n `asset_make` Nullable(String),\n `asset_model` Nullable(String),\n `asset_acquired_date` Nullable(String),\n `asset_disposed_date` Nullable(String),\n `other_asset_details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Engineer_Skills (\n `engineer_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Engineer_Visits (\n `engineer_visit_id` Nullable(Int64),\n `contact_staff_id` Nullable(Int64),\n `engineer_id` Nullable(Int64),\n `fault_log_entry_id` Nullable(Int64),\n `fault_status` Nullable(String),\n `visit_start_datetime` Nullable(String),\n `visit_end_datetime` Nullable(String),\n `other_visit_details` Nullable(String),\n `Engineer_Visits_description` Nullable(String),\n `Engineer_Visits_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log (\n `fault_log_entry_id` Nullable(Int64),\n `asset_id` Nullable(Int64),\n `recorded_by_staff_id` Nullable(Int64),\n `fault_log_entry_datetime` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log_Parts (\n `fault_log_entry_id` Int64,\n `part_fault_id` Int64,\n `fault_status` String\n);\nCREATE TABLE Maintenance_Contracts (\n `maintenance_contract_id` Nullable(Int64),\n `maintenance_contract_company_id` Nullable(Int64),\n `contract_start_date` Nullable(String),\n `contract_end_date` Nullable(String),\n `other_contract_details` Nullable(String),\n `Maintenance_Contracts_description` Nullable(String),\n `Maintenance_Contracts_description_embedding` Array(Float32)\n);\nCREATE TABLE Maintenance_Engineers (\n `engineer_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `other_details` Nullable(String),\n `Maintenance_Engineers_description` Nullable(String),\n `other_details_embedding` Array(Float32),\n `Maintenance_Engineers_description_embedding` Array(Float32)\n);\nCREATE TABLE Part_Faults (\n `part_fault_id` Nullable(Int64),\n `part_id` Nullable(Int64),\n `fault_short_name` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Parts (\n `part_id` Nullable(Int64),\n `part_name` Nullable(String),\n `chargeable_yn` Nullable(String),\n `chargeable_amount` Nullable(String),\n `other_part_details` Nullable(String),\n `Parts_description` Nullable(String),\n `Parts_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills (\n `skill_id` Nullable(Int64),\n `skill_code` Nullable(String),\n `skill_description` Nullable(String),\n `skill_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills_Required_To_Fix (\n `part_fault_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_name` Nullable(String),\n `gender` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `other_staff_details_embedding` Array(Float32),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Third_Party_Companies (\n `company_id` Nullable(Int64),\n `company_type` Nullable(String),\n `company_name` Nullable(String),\n `company_address` Nullable(String),\n `other_company_details` Nullable(String),\n `Third_Party_Companies_description` Nullable(String),\n `other_company_details_embedding` Array(Float32),\n `Third_Party_Companies_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "assets_maintenance", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit related to a critical fault repair') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance\nFROM Engineer_Visits ev\nJOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the IDs of the top 5 engineer visits that are most relevant to handling a critical fault repair?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit for urgent fault resolution') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Visit by engineer to address critical fault') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit focused on critical fault repair') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Handling critical fault during engineer visit') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit to manage urgent fault repair') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Asset_Parts (\n `asset_id` Int64,\n `part_id` Int64\n);\nCREATE TABLE Assets (\n `asset_id` Nullable(Int64),\n `maintenance_contract_id` Nullable(Int64),\n `supplier_company_id` Nullable(Int64),\n `asset_details` Nullable(String),\n `asset_make` Nullable(String),\n `asset_model` Nullable(String),\n `asset_acquired_date` Nullable(String),\n `asset_disposed_date` Nullable(String),\n `other_asset_details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Engineer_Skills (\n `engineer_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Engineer_Visits (\n `engineer_visit_id` Nullable(Int64),\n `contact_staff_id` Nullable(Int64),\n `engineer_id` Nullable(Int64),\n `fault_log_entry_id` Nullable(Int64),\n `fault_status` Nullable(String),\n `visit_start_datetime` Nullable(String),\n `visit_end_datetime` Nullable(String),\n `other_visit_details` Nullable(String),\n `Engineer_Visits_description` Nullable(String),\n `Engineer_Visits_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log (\n `fault_log_entry_id` Nullable(Int64),\n `asset_id` Nullable(Int64),\n `recorded_by_staff_id` Nullable(Int64),\n `fault_log_entry_datetime` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log_Parts (\n `fault_log_entry_id` Int64,\n `part_fault_id` Int64,\n `fault_status` String\n);\nCREATE TABLE Maintenance_Contracts (\n `maintenance_contract_id` Nullable(Int64),\n `maintenance_contract_company_id` Nullable(Int64),\n `contract_start_date` Nullable(String),\n `contract_end_date` Nullable(String),\n `other_contract_details` Nullable(String),\n `Maintenance_Contracts_description` Nullable(String),\n `Maintenance_Contracts_description_embedding` Array(Float32)\n);\nCREATE TABLE Maintenance_Engineers (\n `engineer_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `other_details` Nullable(String),\n `Maintenance_Engineers_description` Nullable(String),\n `other_details_embedding` Array(Float32),\n `Maintenance_Engineers_description_embedding` Array(Float32)\n);\nCREATE TABLE Part_Faults (\n `part_fault_id` Nullable(Int64),\n `part_id` Nullable(Int64),\n `fault_short_name` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Parts (\n `part_id` Nullable(Int64),\n `part_name` Nullable(String),\n `chargeable_yn` Nullable(String),\n `chargeable_amount` Nullable(String),\n `other_part_details` Nullable(String),\n `Parts_description` Nullable(String),\n `Parts_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills (\n `skill_id` Nullable(Int64),\n `skill_code` Nullable(String),\n `skill_description` Nullable(String),\n `skill_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills_Required_To_Fix (\n `part_fault_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_name` Nullable(String),\n `gender` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `other_staff_details_embedding` Array(Float32),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Third_Party_Companies (\n `company_id` Nullable(Int64),\n `company_type` Nullable(String),\n `company_name` Nullable(String),\n `company_address` Nullable(String),\n `other_company_details` Nullable(String),\n `Third_Party_Companies_description` Nullable(String),\n `other_company_details_embedding` Array(Float32),\n `Third_Party_Companies_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "college_3", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old student majoring in Computer Science from Springfield') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID\nFROM Enrolled_in E\nJOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID)\nORDER BY S.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Which courses are linked to the top 5 students resembling a 20-year-old Computer Science major from Springfield? List up to 10 courses.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old Computer Science student from Springfield') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Computer Science major, 20 years old, Springfield native') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Springfield-based 20-year-old studying Computer Science') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A Computer Science student aged 20 from Springfield') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', '20-year-old Springfield student majoring in Computer Science') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Course (\n `CID` Nullable(String),\n `CName` Nullable(String),\n `Credits` Nullable(Int64),\n `Instructor` Nullable(Int64),\n `Days` Nullable(String),\n `Hours` Nullable(String),\n `DNO` Nullable(Int64),\n `Course_description` Nullable(String),\n `Course_description_embedding` Array(Float32)\n);\nCREATE TABLE Department (\n `DNO` Nullable(Int64),\n `Division` Nullable(String),\n `DName` Nullable(String),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `DPhone` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Enrolled_in (\n `StuID` Nullable(Int64),\n `CID` Nullable(String),\n `Grade` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Gradeconversion (\n `lettergrade` Nullable(String),\n `gradepoint` Nullable(Float64)\n);\nCREATE TABLE Member_of (\n `FacID` Nullable(Int64),\n `DNO` Nullable(Int64),\n `Appt_Type` Nullable(String)\n);\nCREATE TABLE Minor_in (\n `StuID` Nullable(Int64),\n `DNO` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "school_player", + "sql": "SELECT s.School\nFROM school s\nINNER JOIN school_details sd ON toString(s.School_ID) = toString(sd.School_ID)\nINNER JOIN school_performance sp ON toString(s.School_ID) = toString(sp.School_Id)\nWHERE s.Year_Entered_Competition IS NOT NULL\nGROUP BY s.School\nORDER BY MAX(s.Enrollment) DESC\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Which school has risen to be the giant in terms of enrollment among those that have taken the leap and entered the competition arena?", + "external_knowledge": "No vector operations are involved in this query, so external knowledge related to vector operations is not applicable.", + "sql_candidate": [ + "SELECT s.School\nFROM school s\nINNER JOIN school_details sd ON toString(s.School_ID) = toString(sd.School_ID)\nINNER JOIN school_performance sp ON toString(s.School_ID) = toString(sp.School_Id)\nWHERE s.Year_Entered_Competition IS NOT NULL\nGROUP BY s.School\nORDER BY MAX(s.Enrollment) DESC\nLIMIT 1;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Team` Nullable(String),\n `Age` Nullable(Int64),\n `Position` Nullable(String),\n `School_ID` Nullable(Int64),\n `player_description` Nullable(String)\n);\nCREATE TABLE school (\n `School_ID` Nullable(Int64),\n `School` Nullable(String),\n `Location` Nullable(String),\n `Enrollment` Nullable(Float64),\n `Founded` Nullable(Float64),\n `Denomination` Nullable(String),\n `Boys_or_Girls` Nullable(String),\n `Day_or_Boarding` Nullable(String),\n `Year_Entered_Competition` Nullable(Float64),\n `School_Colors` Nullable(String),\n `school_description` Nullable(String)\n);\nCREATE TABLE school_details (\n `School_ID` Nullable(Int64),\n `Nickname` Nullable(String),\n `Colors` Nullable(String),\n `League` Nullable(String),\n `Class` Nullable(String),\n `Division` Nullable(String),\n `school_details_description` Nullable(String)\n);\nCREATE TABLE school_performance (\n `School_Id` Nullable(Int64),\n `School_Year` Nullable(String),\n `Class_A` Nullable(String),\n `Class_AA` Nullable(String)\n);" + }, + { + "db_id": "product_catalog", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'chocolate handmade store') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cola with 1 liter capacity') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name\n FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance\n FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name\nFROM Contents_CTE c\nJOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id)\nORDER BY c.distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please identify the two catalog entries that best match the description of a \"Cola with 1 liter capacity\" and are found within catalogs that resemble a \"chocolate handmade store\". List their IDs and names for me!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'artisan chocolate shop') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '1 liter Cola') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'handcrafted chocolate boutique') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'liter-sized Cola drink') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'chocolate artisan store') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cola bottle 1 liter') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'handmade chocolate outlet') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cola with liter capacity') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'chocolate craft store') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '1 liter capacity Cola') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "customer_deliveries", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'durable and eco-friendly materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance\nFROM Products\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the product ID of the top product made with durable and eco-friendly materials?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'high-quality sustainable materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'eco-friendly and robust materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'durable green materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'long-lasting and environmentally friendly materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'sustainable and resilient materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Actual_Order_Products (\n `actual_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Actual_Orders (\n `actual_order_id` Nullable(Int64),\n `order_status_code` String,\n `regular_order_id` Int64,\n `actual_order_date` Nullable(Date)\n);\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `address_type` String,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method` String,\n `customer_name` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `date_became_customer` Nullable(Date),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Delivery_Route_Locations (\n `location_code` Nullable(String),\n `route_id` Int64,\n `location_address_id` Int64,\n `location_name` Nullable(String)\n);\nCREATE TABLE Delivery_Routes (\n `route_id` Nullable(Int64),\n `route_name` Nullable(String),\n `other_route_details` Nullable(String),\n `Delivery_Routes_description` Nullable(String),\n `other_route_details_embedding` Array(Float32)\n);\nCREATE TABLE Employees (\n `employee_id` Nullable(Int64),\n `employee_address_id` Int64,\n `employee_name` Nullable(String),\n `employee_phone` Nullable(String),\n `Employees_description` Nullable(String)\n);\nCREATE TABLE Order_Deliveries (\n `location_code` String,\n `actual_order_id` Int64,\n `delivery_status_code` String,\n `driver_employee_id` Int64,\n `truck_id` Int64,\n `delivery_date` Nullable(Date)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Regular_Order_Products (\n `regular_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Regular_Orders (\n `regular_order_id` Nullable(Int64),\n `distributer_id` Int64\n);\nCREATE TABLE Trucks (\n `truck_id` Nullable(Int64),\n `truck_licence_number` Nullable(String),\n `truck_details` Nullable(String),\n `Trucks_description` Nullable(String)\n);" + }, + { + "db_id": "program_share", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A program originating from Beijing and launched in 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance\nFROM program\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, could you help me find the ID of the program that started in Beijing back in 2004? I'm looking for just the one that best fits this description.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Program initiated in Beijing in the year 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Beijing-based program that commenced in 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Program started in Beijing during 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '2004 launch of a program in Beijing.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A program that began in Beijing in 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE broadcast (\n `Channel_ID` Nullable(Int64),\n `Program_ID` Nullable(Int64),\n `Time_of_day` Nullable(String)\n);\nCREATE TABLE broadcast_share (\n `Channel_ID` Nullable(Int64),\n `Program_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Share_in_percent` Nullable(Float64)\n);\nCREATE TABLE channel (\n `Channel_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Owner` Nullable(String),\n `Share_in_percent` Nullable(Float64),\n `Rating_in_percent` Nullable(Float64),\n `channel_description` Nullable(String),\n `channel_description_embedding` Array(Float32)\n);\nCREATE TABLE program (\n `Program_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Origin` Nullable(String),\n `Launch` Nullable(Float64),\n `Owner` Nullable(String),\n `program_description` Nullable(String),\n `program_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "college_3", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced data structures and algorithms') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance\n FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName\nFROM Enrolled_in e\nJOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID)\nJOIN Course c ON toString(e.CID) = toString(c.CID)\nJOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID)\nJOIN Department d ON toString(mi.DNO) = toString(d.DNO)\nWHERE d.DName LIKE '%Computer Science%'\nORDER BY cm.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the realm of academia, who are the learners weaving through the threads of \"Advanced data structures and algorithms,\" while concurrently dancing within the minor of Computer Science? Reveal their identities and the courses they embrace.", + "external_knowledge": "The `MATCH` operator with `lembed()` in SQLite performs a vector search that helps in identifying items most similar to a given concept, based on embeddings. In this context, \"Advanced data structures and algorithms\" refers to complex course topics involving efficient data organization and problem-solving techniques. The search utilizes embeddings to measure similarity, typically calculated with Euclidean distance, where lower values suggest higher similarity. The `k=5` parameter specifies that we are interested in the top 5 courses that align most closely with the advanced data structures and algorithms concept.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Complex data structures and algorithmic strategies') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced algorithms and data structure techniques') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Data structures and algorithmic complexity') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced computational structures and algorithms') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sophisticated data structures with algorithmic focus') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Course (\n `CID` Nullable(String),\n `CName` Nullable(String),\n `Credits` Nullable(Int64),\n `Instructor` Nullable(Int64),\n `Days` Nullable(String),\n `Hours` Nullable(String),\n `DNO` Nullable(Int64),\n `Course_description` Nullable(String),\n `Course_description_embedding` Array(Float32)\n);\nCREATE TABLE Department (\n `DNO` Nullable(Int64),\n `Division` Nullable(String),\n `DName` Nullable(String),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `DPhone` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Enrolled_in (\n `StuID` Nullable(Int64),\n `CID` Nullable(String),\n `Grade` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Gradeconversion (\n `lettergrade` Nullable(String),\n `gradepoint` Nullable(Float64)\n);\nCREATE TABLE Member_of (\n `FacID` Nullable(Int64),\n `DNO` Nullable(Int64),\n `Appt_Type` Nullable(String)\n);\nCREATE TABLE Minor_in (\n `StuID` Nullable(Int64),\n `DNO` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "customer_deliveries", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'premium quality electronics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '123 Elm St, Springfield, IL') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Main delivery route connecting New York to Boston') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(product_description_embedding, ref_vec_0) AS distance\n FROM ProductSimilarity\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(address_details_embedding, ref_vec_1) AS distance\n FROM Addresses\n\n ORDER BY distance\n LIMIT 3\n),\n\ndr_filtered AS (\n SELECT\n *,\n distance(other_route_details_embedding, ref_vec_2) AS distance\n FROM Delivery_Routes\n\n ORDER BY distance\n LIMIT 2\n),\n\nProductSimilarity AS (\n SELECT p.product_id, p.product_name, distance\n FROM p_filtered AS p\n),\n\nAddressSimilarity AS (\n SELECT a.address_id, distance\n FROM a_filtered AS a\n),\n\nRouteSimilarity AS (\n SELECT dr.route_id, distance\n FROM dr_filtered AS dr\n)\n\nSELECT DISTINCT p.product_name\nFROM ProductSimilarity p\nJOIN Regular_Order_Products rop ON toString(p.product_id) = toString(rop.product_id)\nJOIN Regular_Orders ro ON toString(rop.regular_order_id) = toString(ro.regular_order_id)\nJOIN Actual_Orders ao ON toString(ro.regular_order_id) = toString(ao.regular_order_id)\nJOIN Order_Deliveries od ON toString(ao.actual_order_id) = toString(od.actual_order_id)\nJOIN AddressSimilarity ads ON toString(od.location_code) = toString(ads.address_id)\nJOIN RouteSimilarity rs ON toString(rs.route_id) = toString(od.location_code)\nWHERE ao.order_status_code = 'completed'\nORDER BY p.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the names of products from completed orders? I'm looking for the top 5 products that are known for \"premium quality electronics.\" Also, make sure they were delivered to one of the top 3 addresses similar to \"123 Elm St, Springfield, IL\" and along one of the top 2 routes like the \"Main delivery route connecting New York to Boston.\" Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'high-end electronic devices') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '123 Elm St, Springfield, IL') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Main delivery route connecting New York to Boston') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(product_description_embedding, ref_vec_0) AS distance\n FROM ProductSimilarity\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(address_details_embedding, ref_vec_1) AS distance\n FROM Addresses\n\n ORDER BY distance\n LIMIT 3\n),\n\ndr_filtered AS (\n SELECT\n *,\n distance(other_route_details_embedding, ref_vec_2) AS distance\n FROM Delivery_Routes\n\n ORDER BY distance\n LIMIT 2\n),\n\nProductSimilarity AS (\n SELECT p.product_id, p.product_name, distance FROM p_filtered AS p\n),\n\nAddressSimilarity AS (\n SELECT a.address_id, distance FROM a_filtered AS a\n),\n\nRouteSimilarity AS (\n SELECT dr.route_id, distance FROM dr_filtered AS dr\n)\n\nSELECT DISTINCT p.product_name FROM ProductSimilarity p JOIN Regular_Order_Products rop ON toString(p.product_id) = toString(rop.product_id) JOIN Regular_Orders ro ON toString(rop.regular_order_id) = toString(ro.regular_order_id) JOIN Actual_Orders ao ON toString(ro.regular_order_id) = toString(ao.regular_order_id) JOIN Order_Deliveries od ON toString(ao.actual_order_id) = toString(od.actual_order_id) JOIN AddressSimilarity ads ON toString(od.location_code) = toString(ads.address_id) JOIN RouteSimilarity rs ON toString(rs.route_id) = toString(od.location_code) WHERE ao.order_status_code = 'completed' ORDER BY p.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top-tier electronics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '123 Elm St, Springfield, IL') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Main delivery route connecting New York to Boston') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(product_description_embedding, ref_vec_0) AS distance\n FROM ProductSimilarity\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(address_details_embedding, ref_vec_1) AS distance\n FROM Addresses\n\n ORDER BY distance\n LIMIT 3\n),\n\ndr_filtered AS (\n SELECT\n *,\n distance(other_route_details_embedding, ref_vec_2) AS distance\n FROM Delivery_Routes\n\n ORDER BY distance\n LIMIT 2\n),\n\nProductSimilarity AS (\n SELECT p.product_id, p.product_name, distance FROM p_filtered AS p\n),\n\nAddressSimilarity AS (\n SELECT a.address_id, distance FROM a_filtered AS a\n),\n\nRouteSimilarity AS (\n SELECT dr.route_id, distance FROM dr_filtered AS dr\n)\n\nSELECT DISTINCT p.product_name FROM ProductSimilarity p JOIN Regular_Order_Products rop ON toString(p.product_id) = toString(rop.product_id) JOIN Regular_Orders ro ON toString(rop.regular_order_id) = toString(ro.regular_order_id) JOIN Actual_Orders ao ON toString(ro.regular_order_id) = toString(ao.regular_order_id) JOIN Order_Deliveries od ON toString(ao.actual_order_id) = toString(od.actual_order_id) JOIN AddressSimilarity ads ON toString(od.location_code) = toString(ads.address_id) JOIN RouteSimilarity rs ON toString(rs.route_id) = toString(od.location_code) WHERE ao.order_status_code = 'completed' ORDER BY p.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'premium electronic gadgets') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '123 Elm St, Springfield, IL') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Main delivery route connecting New York to Boston') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(product_description_embedding, ref_vec_0) AS distance\n FROM ProductSimilarity\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(address_details_embedding, ref_vec_1) AS distance\n FROM Addresses\n\n ORDER BY distance\n LIMIT 3\n),\n\ndr_filtered AS (\n SELECT\n *,\n distance(other_route_details_embedding, ref_vec_2) AS distance\n FROM Delivery_Routes\n\n ORDER BY distance\n LIMIT 2\n),\n\nProductSimilarity AS (\n SELECT p.product_id, p.product_name, distance FROM p_filtered AS p\n),\n\nAddressSimilarity AS (\n SELECT a.address_id, distance FROM a_filtered AS a\n),\n\nRouteSimilarity AS (\n SELECT dr.route_id, distance FROM dr_filtered AS dr\n)\n\nSELECT DISTINCT p.product_name FROM ProductSimilarity p JOIN Regular_Order_Products rop ON toString(p.product_id) = toString(rop.product_id) JOIN Regular_Orders ro ON toString(rop.regular_order_id) = toString(ro.regular_order_id) JOIN Actual_Orders ao ON toString(ro.regular_order_id) = toString(ao.regular_order_id) JOIN Order_Deliveries od ON toString(ao.actual_order_id) = toString(od.actual_order_id) JOIN AddressSimilarity ads ON toString(od.location_code) = toString(ads.address_id) JOIN RouteSimilarity rs ON toString(rs.route_id) = toString(od.location_code) WHERE ao.order_status_code = 'completed' ORDER BY p.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'luxury electronics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '123 Elm St, Springfield, IL') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Main delivery route connecting New York to Boston') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(product_description_embedding, ref_vec_0) AS distance\n FROM ProductSimilarity\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(address_details_embedding, ref_vec_1) AS distance\n FROM Addresses\n\n ORDER BY distance\n LIMIT 3\n),\n\ndr_filtered AS (\n SELECT\n *,\n distance(other_route_details_embedding, ref_vec_2) AS distance\n FROM Delivery_Routes\n\n ORDER BY distance\n LIMIT 2\n),\n\nProductSimilarity AS (\n SELECT p.product_id, p.product_name, distance FROM p_filtered AS p\n),\n\nAddressSimilarity AS (\n SELECT a.address_id, distance FROM a_filtered AS a\n),\n\nRouteSimilarity AS (\n SELECT dr.route_id, distance FROM dr_filtered AS dr\n)\n\nSELECT DISTINCT p.product_name FROM ProductSimilarity p JOIN Regular_Order_Products rop ON toString(p.product_id) = toString(rop.product_id) JOIN Regular_Orders ro ON toString(rop.regular_order_id) = toString(ro.regular_order_id) JOIN Actual_Orders ao ON toString(ro.regular_order_id) = toString(ao.regular_order_id) JOIN Order_Deliveries od ON toString(ao.actual_order_id) = toString(od.actual_order_id) JOIN AddressSimilarity ads ON toString(od.location_code) = toString(ads.address_id) JOIN RouteSimilarity rs ON toString(rs.route_id) = toString(od.location_code) WHERE ao.order_status_code = 'completed' ORDER BY p.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'elite quality electronics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '123 Elm St, Springfield, IL') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Main delivery route connecting New York to Boston') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(product_description_embedding, ref_vec_0) AS distance\n FROM ProductSimilarity\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(address_details_embedding, ref_vec_1) AS distance\n FROM Addresses\n\n ORDER BY distance\n LIMIT 3\n),\n\ndr_filtered AS (\n SELECT\n *,\n distance(other_route_details_embedding, ref_vec_2) AS distance\n FROM Delivery_Routes\n\n ORDER BY distance\n LIMIT 2\n),\n\nProductSimilarity AS (\n SELECT p.product_id, p.product_name, distance FROM p_filtered AS p\n),\n\nAddressSimilarity AS (\n SELECT a.address_id, distance FROM a_filtered AS a\n),\n\nRouteSimilarity AS (\n SELECT dr.route_id, distance FROM dr_filtered AS dr\n)\n\nSELECT DISTINCT p.product_name FROM ProductSimilarity p JOIN Regular_Order_Products rop ON toString(p.product_id) = toString(rop.product_id) JOIN Regular_Orders ro ON toString(rop.regular_order_id) = toString(ro.regular_order_id) JOIN Actual_Orders ao ON toString(ro.regular_order_id) = toString(ao.regular_order_id) JOIN Order_Deliveries od ON toString(ao.actual_order_id) = toString(od.actual_order_id) JOIN AddressSimilarity ads ON toString(od.location_code) = toString(ads.address_id) JOIN RouteSimilarity rs ON toString(rs.route_id) = toString(od.location_code) WHERE ao.order_status_code = 'completed' ORDER BY p.distance;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table customer_deliveries.ProductSimilarity does not exist. (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Actual_Order_Products (\n `actual_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Actual_Orders (\n `actual_order_id` Nullable(Int64),\n `order_status_code` String,\n `regular_order_id` Int64,\n `actual_order_date` Nullable(Date)\n);\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `address_type` String,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method` String,\n `customer_name` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `date_became_customer` Nullable(Date),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Delivery_Route_Locations (\n `location_code` Nullable(String),\n `route_id` Int64,\n `location_address_id` Int64,\n `location_name` Nullable(String)\n);\nCREATE TABLE Delivery_Routes (\n `route_id` Nullable(Int64),\n `route_name` Nullable(String),\n `other_route_details` Nullable(String),\n `Delivery_Routes_description` Nullable(String),\n `other_route_details_embedding` Array(Float32)\n);\nCREATE TABLE Employees (\n `employee_id` Nullable(Int64),\n `employee_address_id` Int64,\n `employee_name` Nullable(String),\n `employee_phone` Nullable(String),\n `Employees_description` Nullable(String)\n);\nCREATE TABLE Order_Deliveries (\n `location_code` String,\n `actual_order_id` Int64,\n `delivery_status_code` String,\n `driver_employee_id` Int64,\n `truck_id` Int64,\n `delivery_date` Nullable(Date)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Regular_Order_Products (\n `regular_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Regular_Orders (\n `regular_order_id` Nullable(Int64),\n `distributer_id` Int64\n);\nCREATE TABLE Trucks (\n `truck_id` Nullable(Int64),\n `truck_licence_number` Nullable(String),\n `truck_details` Nullable(String),\n `Trucks_description` Nullable(String)\n);" + }, + { + "db_id": "theme_gallery", + "sql": "SELECT e.Theme, SUM(er.Attendance) AS Total_Attendance\nFROM exhibition e\nJOIN exhibition_record er ON toString(e.Exhibition_ID) = toString(er.Exhibition_ID)\nWHERE e.Year = 2022\nGROUP BY e.Theme\nHAVING SUM(er.Attendance) > 1000\nORDER BY Total_Attendance DESC;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Find the themes of exhibitions held in the year 2022 where the total attendance exceeded 1000, and return the themes along with their total attendance figures, ordered by attendance from highest to lowest.", + "external_knowledge": "", + "sql_candidate": [ + "SELECT e.Theme, SUM(er.Attendance) AS Total_Attendance\nFROM exhibition e\nJOIN exhibition_record er ON toString(e.Exhibition_ID) = toString(er.Exhibition_ID)\nWHERE e.Year = 2022\nGROUP BY e.Theme\nHAVING SUM(er.Attendance) > 1000\nORDER BY Total_Attendance DESC;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Year_Join` Nullable(Int64),\n `Age` Nullable(Int64),\n `artist_description` Nullable(String)\n);\nCREATE TABLE exhibition (\n `Exhibition_ID` Nullable(Int64),\n `Year` Nullable(Int64),\n `Theme` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `Ticket_Price` Nullable(Float64),\n `exhibition_description` Nullable(String)\n);\nCREATE TABLE exhibition_record (\n `Exhibition_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Attendance` Nullable(Int64)\n);" + }, + { + "db_id": "county_public_safety", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'High Hispanic population') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Safe community') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ncps_filtered AS (\n SELECT\n *,\n distance(county_public_safety_description_embedding, ref_vec_1) AS distance\n FROM county_public_safety\n\n ORDER BY distance\n LIMIT 5\n),\n\nCitySafety AS (\n SELECT \n c.City_ID AS City_ID,\n c.Name AS CityName,\n cps.Name AS CountyName,\n c.Hispanic AS Hispanic,\n cps.Population AS Population,\n cps.Police_officers AS Police_officers,\n cps.Crime_rate AS Crime_rate,\n c.city_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'High Hispanic population') AND c.k = 3,\n cps.county_public_safety_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Safe community') AND cps.k = 5\n FROM c_filtered AS c\n JOIN cps_filtered AS cps ON toString(c.County_ID) = toString(cps.County_ID)\n WHERE c.Hispanic > 50.0 ORDER BY \n c.City_ID AS City_ID\n)\n\nSELECT \n CityName,\n CountyName\nFROM \n CitySafety\nWHERE \n Population > 100000\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find up to 10 cities with a Hispanic population greater than 50%, located in counties known for being safe communities, and having populations over 100,000? Also, ensure these cities best represent having a high Hispanic population, and list their names along with the counties they belong to!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cities with significant Hispanic communities') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Safe community') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ncps_filtered AS (\n SELECT\n *,\n distance(county_public_safety_description_embedding, ref_vec_1) AS distance\n FROM county_public_safety\n\n ORDER BY distance\n LIMIT 5\n),\n\nCitySafety AS (\n SELECT c.City_ID, c.Name AS CityName, cps.Name AS CountyName, c.Hispanic, cps.Population, cps.Police_officers, cps.Crime_rate, c.city_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Cities with significant Hispanic communities') AND c.k = 3, cps.county_public_safety_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Safe community') AND cps.k = 5 FROM c_filtered AS c JOIN cps_filtered AS cps ON toString(c.County_ID) = toString(cps.County_ID) WHERE c.Hispanic > 50.0 ORDER BY c.City_ID\n)\n\nSELECT CityName, CountyName FROM CitySafety WHERE Population > 100000 LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High percentage of Hispanic residents') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Safe community') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ncps_filtered AS (\n SELECT\n *,\n distance(county_public_safety_description_embedding, ref_vec_1) AS distance\n FROM county_public_safety\n\n ORDER BY distance\n LIMIT 5\n),\n\nCitySafety AS (\n SELECT c.City_ID, c.Name AS CityName, cps.Name AS CountyName, c.Hispanic, cps.Population, cps.Police_officers, cps.Crime_rate, c.city_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'High percentage of Hispanic residents') AND c.k = 3, cps.county_public_safety_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Safe community') AND cps.k = 5 FROM c_filtered AS c JOIN cps_filtered AS cps ON toString(c.County_ID) = toString(cps.County_ID) WHERE c.Hispanic > 50.0 ORDER BY c.City_ID\n)\n\nSELECT CityName, CountyName FROM CitySafety WHERE Population > 100000 LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cities with large Hispanic populations') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Safe community') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ncps_filtered AS (\n SELECT\n *,\n distance(county_public_safety_description_embedding, ref_vec_1) AS distance\n FROM county_public_safety\n\n ORDER BY distance\n LIMIT 5\n),\n\nCitySafety AS (\n SELECT c.City_ID, c.Name AS CityName, cps.Name AS CountyName, c.Hispanic, cps.Population, cps.Police_officers, cps.Crime_rate, c.city_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Cities with large Hispanic populations') AND c.k = 3, cps.county_public_safety_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Safe community') AND cps.k = 5 FROM c_filtered AS c JOIN cps_filtered AS cps ON toString(c.County_ID) = toString(cps.County_ID) WHERE c.Hispanic > 50.0 ORDER BY c.City_ID\n)\n\nSELECT CityName, CountyName FROM CitySafety WHERE Population > 100000 LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cities exemplifying Hispanic culture') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Safe community') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ncps_filtered AS (\n SELECT\n *,\n distance(county_public_safety_description_embedding, ref_vec_1) AS distance\n FROM county_public_safety\n\n ORDER BY distance\n LIMIT 5\n),\n\nCitySafety AS (\n SELECT c.City_ID, c.Name AS CityName, cps.Name AS CountyName, c.Hispanic, cps.Population, cps.Police_officers, cps.Crime_rate, c.city_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Cities exemplifying Hispanic culture') AND c.k = 3, cps.county_public_safety_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Safe community') AND cps.k = 5 FROM c_filtered AS c JOIN cps_filtered AS cps ON toString(c.County_ID) = toString(cps.County_ID) WHERE c.Hispanic > 50.0 ORDER BY c.City_ID\n)\n\nSELECT CityName, CountyName FROM CitySafety WHERE Population > 100000 LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prominent Hispanic cities') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Safe community') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ncps_filtered AS (\n SELECT\n *,\n distance(county_public_safety_description_embedding, ref_vec_1) AS distance\n FROM county_public_safety\n\n ORDER BY distance\n LIMIT 5\n),\n\nCitySafety AS (\n SELECT c.City_ID, c.Name AS CityName, cps.Name AS CountyName, c.Hispanic, cps.Population, cps.Police_officers, cps.Crime_rate, c.city_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Prominent Hispanic cities') AND c.k = 3, cps.county_public_safety_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Safe community') AND cps.k = 5 FROM c_filtered AS c JOIN cps_filtered AS cps ON toString(c.County_ID) = toString(cps.County_ID) WHERE c.Hispanic > 50.0 ORDER BY c.City_ID\n)\n\nSELECT CityName, CountyName FROM CitySafety WHERE Population > 100000 LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17609 ('[') (line 34, col 44): [0.10508675873279572, -0.03828395903110504, -0.03502754867076874, 0.051357779651880264, -0.10321265459060669, -0.024794073775410652, -0.010893790051341057, -0.0. Expected one of: token, Comma, FROM, PREWHERE, WHERE, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE city (\n `City_ID` Nullable(Int64),\n `County_ID` Nullable(Int64),\n `Name` Nullable(String),\n `White` Nullable(Float64),\n `Black` Nullable(Float64),\n `Amerindian` Nullable(Float64),\n `Asian` Nullable(Float64),\n `Multiracial` Nullable(Float64),\n `Hispanic` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE county_public_safety (\n `County_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Police_officers` Nullable(Int64),\n `Residents_per_officer` Nullable(Int64),\n `Case_burden` Nullable(Int64),\n `Crime_rate` Nullable(Float64),\n `Police_force` Nullable(String),\n `Location` Nullable(String),\n `county_public_safety_description` Nullable(String),\n `county_public_safety_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "railway", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The locomotive was originally built at Midland Railway Works and is known for its iconic wheel arrangement.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance\nFROM railway\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Unearth the railway whose narrative spins around the Midland Railway Works and its famed wheel tapestry.", + "external_knowledge": "The `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search to find vectors in a column that are closest to a given query vector. In this case, the query vector is created using the `lembed('all-MiniLM-L6-v2', ...)` function, which converts the specified text into a vector using the MiniLM language model. The results are ranked by similarity, which is determined by calculating the Euclidean distance (L2 norm) between the vectors; smaller distances indicate higher similarity. The `LIMIT 1` clause ensures that only the most similar railway description is returned. The search mechanism is designed to find semantically similar entries without explicitly relying on keyword matching.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The story of the train revolves around the Midland Railway Works and its renowned wheel design.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Famous for its wheel tapestry, this railway piece was crafted at the Midland Railway Works.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Midland Railway Works is central to the tale of this train, celebrated for its distinctive wheel configuration.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'This railway narrative highlights the Midland Railway Works and its legendary wheel arrangement.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Known for its famous wheel tapestry, this engine was constructed at the Midland Railway Works.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE manager (\n `Manager_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Working_year_starts` Nullable(String),\n `Age` Nullable(Int64),\n `Level` Nullable(Int64),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE railway (\n `Railway_ID` Nullable(Int64),\n `Railway` Nullable(String),\n `Builder` Nullable(String),\n `Built` Nullable(String),\n `Wheels` Nullable(String),\n `Location` Nullable(String),\n `ObjectNumber` Nullable(String),\n `railway_description` Nullable(String),\n `railway_description_embedding` Array(Float32)\n);\nCREATE TABLE railway_manage (\n `Railway_ID` Nullable(Int64),\n `Manager_ID` Nullable(Int64),\n `From_Year` Nullable(String)\n);\nCREATE TABLE train (\n `Train_ID` Nullable(Int64),\n `Train_Num` Nullable(String),\n `Name` Nullable(String),\n `From` Nullable(String),\n `Arrival` Nullable(String),\n `Railway_ID` Nullable(Int64),\n `train_description` Nullable(String),\n `train_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary player inducted with overwhelming support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance\nFROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 6, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top 5 legendary players who got inducted with tons of support? I'd love to know their player IDs, the year they were inducted, who voted for them, how many ballots and votes they got, and how closely they matched this legendary status!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top legendary players with significant induction support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary athletes inducted with high voter approval') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players inducted as legends with strong backing') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hall of Fame legends with substantial support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary figures inducted with extensive voter support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);" + }, + { + "db_id": "wine_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A 2010 Chardonnay from Sonoma County, priced at $25 with a score of 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance\nFROM wine\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need to find the name of the wine that best fits the description of a 2010 Chardonnay from Sonoma County, priced at $25 and scored at 90. Could you identify the top match?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Sonoma County 2010 Chardonnay, $25 price, 90 score.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Chardonnay from 2010 in Sonoma, $25 and rated 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '2010 Sonoma Chardonnay, priced $25, score of 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best match for 2010 Chardonnay, Sonoma County, $25, score 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top 2010 Sonoma Chardonnay, $25, 90 points.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE appellations (\n `No` Nullable(Int64),\n `Appelation` Nullable(String),\n `County` Nullable(String),\n `State` Nullable(String),\n `Area` Nullable(String),\n `isAVA` Nullable(String),\n `appellations_description` Nullable(String),\n `appellations_description_embedding` Array(Float32)\n);\nCREATE TABLE grapes (\n `ID` Nullable(Int64),\n `Grape` Nullable(String),\n `Color` Nullable(String),\n `grapes_description` Nullable(String),\n `grapes_description_embedding` Array(Float32)\n);\nCREATE TABLE wine (\n `No` Nullable(Int64),\n `Grape` Nullable(String),\n `Winery` Nullable(String),\n `Appelation` Nullable(String),\n `State` Nullable(String),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Price` Nullable(Int64),\n `Score` Nullable(Int64),\n `Cases` Nullable(Int64),\n `Drink` Nullable(String),\n `wine_description` Nullable(String),\n `wine_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "products_gen_characteristics", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Herbal Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT \n p.product_id AS product_id,\n p.color_code AS color_code,\n p.product_category_code AS product_category_code,\n distance(p.product_description_embedding, ref_vec_0) AS distance\n FROM \n Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id\nFROM ProductSimilarity ps\nJOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code)\nJOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code)\nORDER BY ps.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the product ID for the top product that matches the concept of \"Herbal Tea\", considering the color and category details?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Natural Herbal Infusion') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Herbal Blend Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Botanical Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tea with Herbal Notes') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Herbal Infused Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Characteristics (\n `characteristic_id` Nullable(Int64),\n `characteristic_type_code` Nullable(String),\n `characteristic_data_type` Nullable(String),\n `characteristic_name` Nullable(String),\n `other_characteristic_details` Nullable(String),\n `Characteristics_description` Nullable(String),\n `other_characteristic_details_embedding` Array(Float32)\n);\nCREATE TABLE Product_Characteristics (\n `product_id` Int64,\n `characteristic_id` Int64,\n `product_characteristic_value` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `color_code` Nullable(String),\n `product_category_code` Nullable(String),\n `product_name` Nullable(String),\n `typical_buying_price` Nullable(String),\n `typical_selling_price` Nullable(String),\n `product_description` Nullable(String),\n `other_product_details` Nullable(String),\n `product_description_embedding` Array(Float32),\n `other_product_details_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Characteristic_Types (\n `characteristic_type_code` Nullable(String),\n `characteristic_type_description` Nullable(String),\n `characteristic_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Colors (\n `color_code` Nullable(String),\n `color_description` Nullable(String),\n `color_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Product_Categories (\n `product_category_code` Nullable(String),\n `product_category_description` Nullable(String),\n `unit_of_measure` Nullable(String),\n `product_category_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in the national league') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance\nFROM player_award pa\nJOIN player p ON toString(pa.player_id) = toString(p.player_id)\nWHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the top 3 players recognized for their outstanding performance in the national league, and provide their full names and award IDs, specifically including those players born in 1990 or originating from the United States.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top players in national league performance') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional national league achievements') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recognized for national league excellence') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in national sports league') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top performers in the national league') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);" + }, + { + "db_id": "real_estate_properties", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Amenity features include facilities such as pools that enhance comfort') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance\nFROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 feature IDs that relate to amenity features enhancing comfort, like having pools? I need them for an upcoming report!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Comfort-enhancing amenities like pools for relaxation') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Features that boost comfort with amenities such as swimming pools') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Amenities that enhance comfort, including pool facilities') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Comfortable living features like pools for leisure') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Facilities such as pools that improve comfort and relaxation') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Other_Available_Features (\n `feature_id` Nullable(Int64),\n `feature_type_code` Nullable(String),\n `feature_name` Nullable(String),\n `feature_description` Nullable(String),\n `feature_description_embedding` Array(Float32)\n);\nCREATE TABLE Other_Available_Features_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Other_Property_Features (\n `property_id` Int64,\n `feature_id` Int64,\n `property_feature_description` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` String,\n `date_on_market` Nullable(Date),\n `date_sold` Nullable(Date),\n `property_name` Nullable(String),\n `property_address` Nullable(String),\n `room_count` Nullable(Int64),\n `vendor_requested_price` Nullable(Decimal(38, 6)),\n `buyer_offered_price` Nullable(Decimal(38, 6)),\n `agreed_selling_price` Nullable(Decimal(38, 6)),\n `apt_feature_1` Nullable(String),\n `apt_feature_2` Nullable(String),\n `apt_feature_3` Nullable(String),\n `fld_feature_1` Nullable(String),\n `fld_feature_2` Nullable(String),\n `fld_feature_3` Nullable(String),\n `hse_feature_1` Nullable(String),\n `hse_feature_2` Nullable(String),\n `hse_feature_3` Nullable(String),\n `oth_feature_1` Nullable(String),\n `oth_feature_2` Nullable(String),\n `oth_feature_3` Nullable(String),\n `shp_feature_1` Nullable(String),\n `shp_feature_2` Nullable(String),\n `shp_feature_3` Nullable(String),\n `other_property_details` Nullable(String),\n `Properties_description` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types (\n `feature_type_code` Nullable(String),\n `feature_type_name` Nullable(String),\n `Ref_Feature_Types_description` Nullable(String),\n `Ref_Feature_Types_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types (\n `property_type_code` Nullable(String),\n `property_type_description` Nullable(String),\n `property_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Property_Types_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance\nFROM player_award p\nJOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the player IDs of the 10 players who excelled the most during the baseball season?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top performance during the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best players of the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional achievements in baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Most successful players in the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players with outstanding contributions in the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned university located in a major city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance\nFROM college\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which college is the most renowned and is situated in a major city?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A prestigious college located in a major urban area') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A famous college situated in a large city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An acclaimed university in a prominent city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known college in a big metropolitan area') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A distinguished university located in a major city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);" + }, + { + "db_id": "csu_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A university that opened in the early 2000s in a coastal area') AS ref_vec_0,\n\nSimilarCampuses AS (\n SELECT c.Id, c.Campus, c.Location, c.County, distance(c.Campuses_description_embedding, ref_vec_0) AS distance\n FROM Campuses c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n sc.Campus AS Campus, \n sc.Location AS Location, \n e.TotalEnrollment_AY AS YearlyEnrollment, \n d.Degrees AS YearlyDegrees, \n AVG(f.Faculty) AS AvgFaculty\nFROM \n SimilarCampuses sc\nJOIN \n enrollments e ON toString(sc.Id) = toString(e.Campus)\nJOIN \n degrees d ON toString(sc.Id) = toString(d.Campus) AND e.Year = d.Year\nJOIN \n faculty f ON toString(sc.Id) = toString(f.Campus) AND e.Year = f.Year\nGROUP BY \n sc.Campus, sc.Location, e.Year, d.Degrees\nORDER BY \n sc.Campus, e.Year;", + "sql_result_column_count": 5, + "sql_result_rows_count": 15, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you fetch details about the 5 university campuses that best match the description of \"a university that opened in the early 2000s in a coastal area\"? Specifically, I need to know their names, locations, yearly enrollment numbers, yearly degrees awarded, and average number of faculty members, ordered by campus name and year.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A university established in the early 2000s near the coast') AS ref_vec_0,\n\nSimilarCampuses AS (\n SELECT c.Id, c.Campus, c.Location, c.County, distance(c.Campuses_description_embedding, ref_vec_0) AS distance FROM Campuses c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sc.Campus, sc.Location, e.TotalEnrollment_AY AS YearlyEnrollment, d.Degrees AS YearlyDegrees, AVG(f.Faculty) AS AvgFaculty FROM SimilarCampuses sc JOIN enrollments e ON toString(sc.Id) = toString(e.Campus) JOIN degrees d ON toString(sc.Id) = toString(d.Campus) AND e.Year = d.Year JOIN faculty f ON toString(sc.Id) = toString(f.Campus) AND e.Year = f.Year GROUP BY sc.Campus, sc.Location, e.Year, d.Degrees ORDER BY sc.Campus, e.Year;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A university founded in the early 2000s along the coastline') AS ref_vec_0,\n\nSimilarCampuses AS (\n SELECT c.Id, c.Campus, c.Location, c.County, distance(c.Campuses_description_embedding, ref_vec_0) AS distance FROM Campuses c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sc.Campus, sc.Location, e.TotalEnrollment_AY AS YearlyEnrollment, d.Degrees AS YearlyDegrees, AVG(f.Faculty) AS AvgFaculty FROM SimilarCampuses sc JOIN enrollments e ON toString(sc.Id) = toString(e.Campus) JOIN degrees d ON toString(sc.Id) = toString(d.Campus) AND e.Year = d.Year JOIN faculty f ON toString(sc.Id) = toString(f.Campus) AND e.Year = f.Year GROUP BY sc.Campus, sc.Location, e.Year, d.Degrees ORDER BY sc.Campus, e.Year;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A university initiated in the early 2000s situated by the sea') AS ref_vec_0,\n\nSimilarCampuses AS (\n SELECT c.Id, c.Campus, c.Location, c.County, distance(c.Campuses_description_embedding, ref_vec_0) AS distance FROM Campuses c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sc.Campus, sc.Location, e.TotalEnrollment_AY AS YearlyEnrollment, d.Degrees AS YearlyDegrees, AVG(f.Faculty) AS AvgFaculty FROM SimilarCampuses sc JOIN enrollments e ON toString(sc.Id) = toString(e.Campus) JOIN degrees d ON toString(sc.Id) = toString(d.Campus) AND e.Year = d.Year JOIN faculty f ON toString(sc.Id) = toString(f.Campus) AND e.Year = f.Year GROUP BY sc.Campus, sc.Location, e.Year, d.Degrees ORDER BY sc.Campus, e.Year;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A university that began operations in the early 2000s in a coastal region') AS ref_vec_0,\n\nSimilarCampuses AS (\n SELECT c.Id, c.Campus, c.Location, c.County, distance(c.Campuses_description_embedding, ref_vec_0) AS distance FROM Campuses c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sc.Campus, sc.Location, e.TotalEnrollment_AY AS YearlyEnrollment, d.Degrees AS YearlyDegrees, AVG(f.Faculty) AS AvgFaculty FROM SimilarCampuses sc JOIN enrollments e ON toString(sc.Id) = toString(e.Campus) JOIN degrees d ON toString(sc.Id) = toString(d.Campus) AND e.Year = d.Year JOIN faculty f ON toString(sc.Id) = toString(f.Campus) AND e.Year = f.Year GROUP BY sc.Campus, sc.Location, e.Year, d.Degrees ORDER BY sc.Campus, e.Year;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A university inaugurated in the early 2000s near a coastal area') AS ref_vec_0,\n\nSimilarCampuses AS (\n SELECT c.Id, c.Campus, c.Location, c.County, distance(c.Campuses_description_embedding, ref_vec_0) AS distance FROM Campuses c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sc.Campus, sc.Location, e.TotalEnrollment_AY AS YearlyEnrollment, d.Degrees AS YearlyDegrees, AVG(f.Faculty) AS AvgFaculty FROM SimilarCampuses sc JOIN enrollments e ON toString(sc.Id) = toString(e.Campus) JOIN degrees d ON toString(sc.Id) = toString(d.Campus) AND e.Year = d.Year JOIN faculty f ON toString(sc.Id) = toString(f.Campus) AND e.Year = f.Year GROUP BY sc.Campus, sc.Location, e.Year, d.Degrees ORDER BY sc.Campus, e.Year;" + ], + "integration_level": 4, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 215, server response: Code: 215. DB::Exception: Column `TotalEnrollment_AY` is not under aggregate function and not in GROUP BY. Have columns: ['Degrees','_--e.Year','avg(Faculty)','_--sc.Location','_--sc.Campus']: While processing `_--sc.Campus` AS Campus, `_--sc.Location` AS Location, TotalEnrollment_AY AS YearlyEnrollment, Degrees AS YearlyDegrees, avg(Faculty) AS AvgFaculty. (NOT_AN_AGGREGATE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Campuses (\n `Id` Nullable(Int64),\n `Campus` Nullable(String),\n `Location` Nullable(String),\n `County` Nullable(String),\n `Year` Nullable(Int64),\n `Campuses_description` Nullable(String),\n `Campuses_description_embedding` Array(Float32)\n);\nCREATE TABLE csu_fees (\n `Campus` Nullable(Int64),\n `Year` Nullable(Int64),\n `CampusFee` Nullable(Int64)\n);\nCREATE TABLE degrees (\n `Year` Nullable(Int64),\n `Campus` Nullable(Int64),\n `Degrees` Nullable(Int64)\n);\nCREATE TABLE discipline_enrollments (\n `Campus` Nullable(Int64),\n `Discipline` Nullable(Int64),\n `Year` Nullable(Int64),\n `Undergraduate` Nullable(Int64),\n `Graduate` Nullable(Int64)\n);\nCREATE TABLE enrollments (\n `Campus` Nullable(Int64),\n `Year` Nullable(Int64),\n `TotalEnrollment_AY` Nullable(Int64),\n `FTE_AY` Nullable(Int64)\n);\nCREATE TABLE faculty (\n `Campus` Nullable(Int64),\n `Year` Nullable(Int64),\n `Faculty` Nullable(Float64)\n);" + }, + { + "db_id": "manufacturer", + "sql": "WITH ManufacturerDetails AS (\n SELECT \n m.Manufacturer_ID AS Manufacturer_ID, \n m.Name AS Manufacturer_Name, \n m.Num_of_Factories AS Num_of_Factories, \n m.Num_of_Shops AS Num_of_Shops\n FROM manufacturer m\n), FurnitureDetails AS (\n SELECT \n f.Furniture_ID AS Furniture_ID,\n f.Name AS Furniture_Name,\n f.Num_of_Component AS Num_of_Component,\n f.Market_Rate AS Market_Rate\n FROM furniture f\n), LatestManufacturerFurniture AS (\n SELECT \n fm.Manufacturer_ID AS Manufacturer_ID, \n fm.Furniture_ID AS Furniture_ID,\n fm.Price_in_Dollar AS Price_in_Dollar\n FROM furniture_manufacte fm\n INNER JOIN ManufacturerDetails md ON toString(fm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n WHERE fm.Price_in_Dollar > 1000 \n), CombinedData AS (\n SELECT \n lm.Manufacturer_ID AS Manufacturer_ID,\n lm.Furniture_ID AS Furniture_ID,\n md.Manufacturer_Name AS Manufacturer_Name,\n fd.Furniture_Name AS Furniture_Name,\n lm.Price_in_Dollar AS Price_in_Dollar,\n fd.Market_Rate AS Market_Rate,\n ROW_NUMBER() OVER (PARTITION BY lm.Manufacturer_ID ORDER BY lm.Price_in_Dollar DESC) AS rn\n FROM LatestManufacturerFurniture lm\n INNER JOIN ManufacturerDetails md ON toString(lm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n INNER JOIN FurnitureDetails fd ON toString(lm.Furniture_ID) = toString(fd.Furniture_ID)\n)\n\n\nSELECT Manufacturer_Name\nFROM CombinedData\nWHERE rn = 1\nORDER BY Manufacturer_Name;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which companies are crafting the priciest furniture pieces that stand out from the rest?", + "external_knowledge": "In this context, vector operations like \"MATCH\" or ANN search are not utilized. The query involves typical SQL operations like filtering, joining, and ordering dataset entries based on specific criteria, such as price. When dealing with vector search operations, concepts like Euclidean distance and k-nearest neighbors are crucial, but they are not applicable here as the query does not perform such tasks. The focus is on identifying items with high monetary value rather than similarity or distance metrics.", + "sql_candidate": [ + "WITH ManufacturerDetails AS (\n SELECT \n m.Manufacturer_ID AS Manufacturer_ID, \n m.Name AS Manufacturer_Name, \n m.Num_of_Factories AS Num_of_Factories, \n m.Num_of_Shops AS Num_of_Shops\n FROM manufacturer m\n), FurnitureDetails AS (\n SELECT \n f.Furniture_ID AS Furniture_ID,\n f.Name AS Furniture_Name,\n f.Num_of_Component AS Num_of_Component,\n f.Market_Rate AS Market_Rate\n FROM furniture f\n), LatestManufacturerFurniture AS (\n SELECT \n fm.Manufacturer_ID AS Manufacturer_ID, \n fm.Furniture_ID AS Furniture_ID,\n fm.Price_in_Dollar AS Price_in_Dollar\n FROM furniture_manufacte fm\n INNER JOIN ManufacturerDetails md ON toString(fm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n WHERE fm.Price_in_Dollar > 1000 \n), CombinedData AS (\n SELECT \n lm.Manufacturer_ID AS Manufacturer_ID,\n lm.Furniture_ID AS Furniture_ID,\n md.Manufacturer_Name AS Manufacturer_Name,\n fd.Furniture_Name AS Furniture_Name,\n lm.Price_in_Dollar AS Price_in_Dollar,\n fd.Market_Rate AS Market_Rate,\n ROW_NUMBER() OVER (PARTITION BY lm.Manufacturer_ID ORDER BY lm.Price_in_Dollar DESC) AS rn\n FROM LatestManufacturerFurniture lm\n INNER JOIN ManufacturerDetails md ON toString(lm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n INNER JOIN FurnitureDetails fd ON toString(lm.Furniture_ID) = toString(fd.Furniture_ID)\n)\n\n\nSELECT Manufacturer_Name\nFROM CombinedData\nWHERE rn = 1\nORDER BY Manufacturer_Name;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE furniture (\n `Furniture_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Num_of_Component` Nullable(Int64),\n `Market_Rate` Nullable(Float64),\n `furniture_description` Nullable(String)\n);\nCREATE TABLE furniture_manufacte (\n `Manufacturer_ID` Nullable(Int64),\n `Furniture_ID` Nullable(Int64),\n `Price_in_Dollar` Nullable(Float64)\n);\nCREATE TABLE manufacturer (\n `Manufacturer_ID` Nullable(Int64),\n `Open_Year` Nullable(Float64),\n `Name` Nullable(String),\n `Num_of_Factories` Nullable(Int64),\n `Num_of_Shops` Nullable(Int64),\n `manufacturer_description` Nullable(String)\n);" + }, + { + "db_id": "concert_singer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A large stadium located in the city center with frequent concerts') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance\nFROM stadium\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the bustling orchestra of the city center, where might one find a grand stage that frequently echoes with the melodies of concerts?", + "external_knowledge": "- The `MATCH` operator performs an approximate nearest neighbor (ANN) search, which finds the closest match to a given vector by comparing distances. \n- Vectors are typically compared using Euclidean distance (L2 norm), where similarity increases as distance decreases. \n- The `lembed('all-MiniLM-L6-v2', ...)` function transforms the provided textual description into a vector representation, facilitating this search for matching entities in the database. \n- The description being matched indicates a stadium characterized by size, central location, and concert frequency, which are key elements in the search.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent venue in the city center known for hosting concerts frequently') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A central city stadium with regular concert events') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A major performance venue in the heart of the city with frequent musical events') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A large performance space in downtown often featuring concerts') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A central location for concerts in the city center') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "company_employee", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John, a talented finance analyst from Harvard University, joined a prominent US-based bank.') AS ref_vec_0\n\nSELECT p.People_ID, distance(p.people_description_embedding, ref_vec_0) AS distance\nFROM people p\nJOIN employment e ON toString(p.People_ID) = toString(e.People_ID)\nJOIN company c ON toString(e.Company_ID) = toString(c.Company_ID)\nWHERE c.Headquarters = 'USA'\nAND c.Industry = 'Banking'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you provide the IDs of up to 5 individuals who are described as finance analysts from Harvard University joining major banks in the USA, specifically within the Banking sector?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Finance professionals from Harvard University entering major US banks.') AS ref_vec_0\n\nSELECT p.People_ID, distance(p.people_description_embedding, ref_vec_0) AS distance FROM people p JOIN employment e ON toString(p.People_ID) = toString(e.People_ID) JOIN company c ON toString(e.Company_ID) = toString(c.Company_ID) WHERE c.Headquarters = 'USA' AND c.Industry = 'Banking'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Harvard graduates working as finance analysts at top banks in the United States.') AS ref_vec_0\n\nSELECT p.People_ID, distance(p.people_description_embedding, ref_vec_0) AS distance FROM people p JOIN employment e ON toString(p.People_ID) = toString(e.People_ID) JOIN company c ON toString(e.Company_ID) = toString(c.Company_ID) WHERE c.Headquarters = 'USA' AND c.Industry = 'Banking'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Individuals from Harvard taking finance analyst roles in leading US banks.') AS ref_vec_0\n\nSELECT p.People_ID, distance(p.people_description_embedding, ref_vec_0) AS distance FROM people p JOIN employment e ON toString(p.People_ID) = toString(e.People_ID) JOIN company c ON toString(e.Company_ID) = toString(c.Company_ID) WHERE c.Headquarters = 'USA' AND c.Industry = 'Banking'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Harvard finance analysts joining major banks in the USA.') AS ref_vec_0\n\nSELECT p.People_ID, distance(p.people_description_embedding, ref_vec_0) AS distance FROM people p JOIN employment e ON toString(p.People_ID) = toString(e.People_ID) JOIN company c ON toString(e.Company_ID) = toString(c.Company_ID) WHERE c.Headquarters = 'USA' AND c.Industry = 'Banking'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top US banks hiring finance analysts from Harvard University.') AS ref_vec_0\n\nSELECT p.People_ID, distance(p.people_description_embedding, ref_vec_0) AS distance FROM people p JOIN employment e ON toString(p.People_ID) = toString(e.People_ID) JOIN company c ON toString(e.Company_ID) = toString(c.Company_ID) WHERE c.Headquarters = 'USA' AND c.Industry = 'Banking'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'people_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE company (\n `Company_ID` Nullable(Float64),\n `Name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_in_Billion` Nullable(Float64),\n `Profits_in_Billion` Nullable(Float64),\n `Assets_in_Billion` Nullable(Float64),\n `Market_Value_in_Billion` Nullable(Float64),\n `company_description` Nullable(String),\n `company_description_embedding` Array(Float32)\n);\nCREATE TABLE employment (\n `Company_ID` Nullable(Int64),\n `People_ID` Nullable(Int64),\n `Year_working` Nullable(Int64)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `Age` Nullable(Int64),\n `Name` Nullable(String),\n `Nationality` Nullable(String),\n `Graduation_College` Nullable(String),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "concert_singer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A large stadium with high attendance and modern facilities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance \nFROM stadium\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the stadium that perfectly fits the vibe of being large, having high attendance, and featuring modern facilities? I'd love to know its ID and name!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A spacious stadium known for its large crowds and state-of-the-art amenities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A modern, large-capacity stadium with high visitor numbers') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A big stadium with excellent attendance and contemporary facilities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A large venue with high footfall and modern infrastructure') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A stadium featuring a large size, significant attendance, and up-to-date facilities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "election_representative", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An influential representative known for reform policies.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT \n r.Representative_ID AS Representative_ID, \n r.Name AS Name, \n r.State AS State, \n distance(r.representative_description_embedding, ref_vec_0) AS distance\n FROM \n representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n rm.Representative_ID AS Representative_ID, \n rm.Name AS Name, \n e.Votes AS Votes\nFROM \n RepresentativeMatch rm\nJOIN \n election e ON toString(rm.Representative_ID) = toString(e.Representative_ID)\nORDER BY \n e.Votes DESC\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 4, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Find the top 5 influential representatives known for reform policies and list their vote counts.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top representatives advocating for reform initiatives.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading figures in reform policy advocacy.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Influential lawmakers focused on reform agendas.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prominent representatives pushing for policy reforms.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key advocates of reform policies in the legislature.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Representative_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Votes` Nullable(Float64),\n `Vote_Percent` Nullable(Float64),\n `Seats` Nullable(Float64),\n `Place` Nullable(Float64)\n);\nCREATE TABLE representative (\n `Representative_ID` Nullable(Int64),\n `Name` Nullable(String),\n `State` Nullable(String),\n `Party` Nullable(String),\n `Lifespan` Nullable(String),\n `representative_description` Nullable(String),\n `representative_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "concert_singer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Large stadium in New York with a seating capacity over 50,000') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance\n FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID\n FROM singer\n WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name\nFROM concert c\nJOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID)\nJOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID)\nJOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID)\nWHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of concerts that feature American singers and take place in a large stadium located in New York with a seating capacity of over 50,000?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Major venue in NYC with over 50,000 seats') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Large-capacity stadium in New York with American performers') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York stadium with high seating capacity for concerts') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York concert venues with over 50,000 seats') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium in New York with large seating for American concerts') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "entrepreneur", + "sql": "WITH HighInvestmentEntrepreneurs AS (\n SELECT Entrepreneur_ID, People_ID, Money_Requested\n FROM entrepreneur\n WHERE Money_Requested > 100000\n)\nSELECT p.Name\nFROM HighInvestmentEntrepreneurs hie\nJOIN people p ON hie.People_ID = p.People_ID\nWHERE hie.Entrepreneur_ID IN (\n SELECT Entrepreneur_ID\n FROM entrepreneur\n WHERE entrepreneur_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"Entrepreneur seeking substantial investment in tech.\")\n AND k = 5\n);", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of people who are associated with entrepreneurs requesting over $100,000, and who are among the top 5 entrepreneurs seeking substantial investment in tech?", + "external_knowledge": "", + "sql_candidate": [ + "WITH HighInvestmentEntrepreneurs AS ( SELECT Entrepreneur_ID, People_ID, Money_Requested FROM entrepreneur WHERE Money_Requested > 100000 ) SELECT p.Name FROM HighInvestmentEntrepreneurs hie JOIN people p ON hie.People_ID = p.People_ID WHERE hie.Entrepreneur_ID IN ( SELECT Entrepreneur_ID FROM entrepreneur WHERE entrepreneur_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Top tech entrepreneur seeking major funding') AND k = 5 );", + "WITH HighInvestmentEntrepreneurs AS ( SELECT Entrepreneur_ID, People_ID, Money_Requested FROM entrepreneur WHERE Money_Requested > 100000 ) SELECT p.Name FROM HighInvestmentEntrepreneurs hie JOIN people p ON hie.People_ID = p.People_ID WHERE hie.Entrepreneur_ID IN ( SELECT Entrepreneur_ID FROM entrepreneur WHERE entrepreneur_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Entrepreneur in tech requesting significant investment') AND k = 5 );", + "WITH HighInvestmentEntrepreneurs AS ( SELECT Entrepreneur_ID, People_ID, Money_Requested FROM entrepreneur WHERE Money_Requested > 100000 ) SELECT p.Name FROM HighInvestmentEntrepreneurs hie JOIN people p ON hie.People_ID = p.People_ID WHERE hie.Entrepreneur_ID IN ( SELECT Entrepreneur_ID FROM entrepreneur WHERE entrepreneur_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Leading tech entrepreneur with high funding needs') AND k = 5 );", + "WITH HighInvestmentEntrepreneurs AS ( SELECT Entrepreneur_ID, People_ID, Money_Requested FROM entrepreneur WHERE Money_Requested > 100000 ) SELECT p.Name FROM HighInvestmentEntrepreneurs hie JOIN people p ON hie.People_ID = p.People_ID WHERE hie.Entrepreneur_ID IN ( SELECT Entrepreneur_ID FROM entrepreneur WHERE entrepreneur_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Tech entrepreneur aiming for large-scale investment') AND k = 5 );", + "WITH HighInvestmentEntrepreneurs AS ( SELECT Entrepreneur_ID, People_ID, Money_Requested FROM entrepreneur WHERE Money_Requested > 100000 ) SELECT p.Name FROM HighInvestmentEntrepreneurs hie JOIN people p ON hie.People_ID = p.People_ID WHERE hie.Entrepreneur_ID IN ( SELECT Entrepreneur_ID FROM entrepreneur WHERE entrepreneur_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Innovative tech entrepreneur seeking substantial funding') AND k = 5 );" + ], + "execution_status": "exception", + "error_message": "歧义错误: 在多表查询中发现无别名的向量搜索列 'entrepreneur_description_embedding'。请为该列表明表别名。", + "db_type": "myscale", + "schema": "CREATE TABLE entrepreneur (\n `Entrepreneur_ID` Nullable(Int64),\n `People_ID` Nullable(Int64),\n `Company` Nullable(String),\n `Money_Requested` Nullable(Float64),\n `Investor` Nullable(String),\n `entrepreneur_description` Nullable(String),\n `entrepreneur_description_embedding` Array(Float32)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Height` Nullable(Float64),\n `Weight` Nullable(Float64),\n `Date_of_Birth` Nullable(String),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "swimming", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exciting swimming event held in a large stadium') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in a competitive 200 meters race') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id\nFROM e_filtered AS e\nJOIN record r ON toString(e.ID) = toString(r.Event_ID)\nJOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "sql_result_column_count": 1, + "sql_result_rows_count": 9, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find a selection of events that took place in a large stadium and featured swimmers known for their exceptional performances in 200-meter races?", + "external_knowledge": "- The `MATCH` operator is used here to perform an approximate nearest neighbor (ANN) search on vector embeddings.\n- Embeddings are high-dimensional representations of text used to capture semantic meaning.\n- The `lembed()` function involves transforming textual descriptions into these embeddings using a model like 'all-MiniLM-L6-v2'.\n- The query uses `k=10` to limit the search to the top 10 events and `k=5` for the top 5 swimmers that match the given descriptions.\n- In vector space, similarity is determined using Euclidean distance; lower distances indicate higher similarity.\n- Descriptions like \"an exciting swimming event held in a large stadium\" and \"outstanding performance in a competitive 200 meters race\" are mapped to vectors encapsulating these concepts for comparison.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A major swimming competition hosted in a grand stadium') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Notable achievements in 200-meter swimming events') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A large-scale stadium event featuring top swimmers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Exceptional 200m race performances by swimmers') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent swimming meet in a vast arena') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '200-meter race specialists with remarkable records') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A significant swimming event occurring in a large venue') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Elite swimmers known for 200m excellence') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'An impressive swimming event in a massive stadium') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Swimmers with outstanding 200-meter race skills') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE event (\n `ID` Nullable(Int64),\n `Name` Nullable(String),\n `Stadium_ID` Nullable(Int64),\n `Year` Nullable(String),\n `event_description` Nullable(String),\n `event_description_embedding` Array(Float32)\n);\nCREATE TABLE event_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE record (\n `ID` Nullable(Int64),\n `Result` Nullable(String),\n `Swimmer_ID` Nullable(Int64),\n `Event_ID` Nullable(Int64)\n);\nCREATE TABLE stadium (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `Capacity` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `Opening_year` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);\nCREATE TABLE stadium_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE swimmer (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `Nationality` Nullable(String),\n `meter_100` Nullable(Float64),\n `meter_200` Nullable(String),\n `meter_300` Nullable(String),\n `meter_400` Nullable(String),\n `meter_500` Nullable(String),\n `meter_600` Nullable(String),\n `meter_700` Nullable(String),\n `Time` Nullable(String),\n `swimmer_description` Nullable(String),\n `swimmer_description_embedding` Array(Float32)\n);\nCREATE TABLE swimmer_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "roller_coaster", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A roller coaster with a thrilling experience and high speed') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance\nFROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Top 5 roller coasters known for thrilling experiences and high speeds, list their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exciting roller coasters with high velocity and adrenaline-pumping rides') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Roller coasters offering exhilarating speeds and thrilling experiences') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-speed roller coasters known for their thrilling rides') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Roller coasters renowned for fast and thrilling rides') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top roller coasters with thrilling high-speed experiences') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Area` Nullable(Int64),\n `Languages` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE country_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE roller_coaster (\n `Roller_Coaster_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Park` Nullable(String),\n `Country_ID` Nullable(Int64),\n `Length` Nullable(Float64),\n `Height` Nullable(Float64),\n `Speed` Nullable(String),\n `Opened` Nullable(String),\n `Status` Nullable(String),\n `roller_coaster_description` Nullable(String),\n `roller_coaster_description_embedding` Array(Float32)\n);\nCREATE TABLE roller_coaster_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "roller_coaster", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Wooden roller coaster with a thrilling experience and unique design') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance \nFROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the roller coaster that best embodies a thrilling experience and unique design as a wooden coaster, and provide its ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exciting wooden coaster with innovative design') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Wooden coaster offering a thrilling and unique ride') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Unique wooden coaster that provides an exhilarating experience') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thrilling wooden roller coaster with distinctive design') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Wooden roller coaster known for its thrilling and unique features') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Area` Nullable(Int64),\n `Languages` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE country_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE roller_coaster (\n `Roller_Coaster_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Park` Nullable(String),\n `Country_ID` Nullable(Int64),\n `Length` Nullable(Float64),\n `Height` Nullable(Float64),\n `Speed` Nullable(String),\n `Opened` Nullable(String),\n `Status` Nullable(String),\n `roller_coaster_description` Nullable(String),\n `roller_coaster_description_embedding` Array(Float32)\n);\nCREATE TABLE roller_coaster_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "college_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Room with advanced facilities for research') AS ref_vec_0,\n\nsimilar_classrooms AS (\n SELECT building, room_number, distance(classroom.classroom_description_embedding, ref_vec_0) AS distance\n FROM classroom\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.name\nFROM instructor i\nJOIN teaches t ON toString(i.ID) = toString(t.ID)\nJOIN section s ON toString(t.course_id) = toString(s.course_id) AND t.sec_id = s.sec_id AND t.semester = s.semester AND t.year = s.year\nJOIN similar_classrooms sc ON toString(s.building) = toString(sc.building) AND s.room_number = sc.room_number\nORDER BY sc.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Which instructors are leading the charge in the top 5 rooms equipped like innovative research hubs?", + "external_knowledge": "The \"MATCH\" operator in the SQL query is used for performing an approximate nearest neighbor (ANN) search on vector embeddings, which allows for finding items that are semantically similar to a given concept. In this case, the embeddings are compared using Euclidean distance, where a lower distance indicates a closer match to the concept of \"Room with advanced facilities for research.\" The parameter \"k = 5\" specifies that the query retrieves the top 5 classrooms most closely matching this description. This vector operation is facilitated by the \"sqlite-vec\" and \"sqlite-lembed\" extensions, which handle vector data and enable efficient similarity searches within the database.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative research hub facilities') AS ref_vec_0,\n\nsimilar_classrooms AS (\n SELECT building, room_number, distance(classroom.classroom_description_embedding, ref_vec_0) AS distance FROM classroom\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.name FROM instructor i JOIN teaches t ON toString(i.ID) = toString(t.ID) JOIN section s ON toString(t.course_id) = toString(s.course_id) AND t.sec_id = s.sec_id AND t.semester = s.semester AND t.year = s.year JOIN similar_classrooms sc ON toString(s.building) = toString(sc.building) AND s.room_number = sc.room_number ORDER BY sc.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-tier research amenities') AS ref_vec_0,\n\nsimilar_classrooms AS (\n SELECT building, room_number, distance(classroom.classroom_description_embedding, ref_vec_0) AS distance FROM classroom\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.name FROM instructor i JOIN teaches t ON toString(i.ID) = toString(t.ID) JOIN section s ON toString(t.course_id) = toString(s.course_id) AND t.sec_id = s.sec_id AND t.semester = s.semester AND t.year = s.year JOIN similar_classrooms sc ON toString(s.building) = toString(sc.building) AND s.room_number = sc.room_number ORDER BY sc.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rooms equipped for cutting-edge research') AS ref_vec_0,\n\nsimilar_classrooms AS (\n SELECT building, room_number, distance(classroom.classroom_description_embedding, ref_vec_0) AS distance FROM classroom\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.name FROM instructor i JOIN teaches t ON toString(i.ID) = toString(t.ID) JOIN section s ON toString(t.course_id) = toString(s.course_id) AND t.sec_id = s.sec_id AND t.semester = s.semester AND t.year = s.year JOIN similar_classrooms sc ON toString(s.building) = toString(sc.building) AND s.room_number = sc.room_number ORDER BY sc.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced research environment') AS ref_vec_0,\n\nsimilar_classrooms AS (\n SELECT building, room_number, distance(classroom.classroom_description_embedding, ref_vec_0) AS distance FROM classroom\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.name FROM instructor i JOIN teaches t ON toString(i.ID) = toString(t.ID) JOIN section s ON toString(t.course_id) = toString(s.course_id) AND t.sec_id = s.sec_id AND t.semester = s.semester AND t.year = s.year JOIN similar_classrooms sc ON toString(s.building) = toString(sc.building) AND s.room_number = sc.room_number ORDER BY sc.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'State-of-the-art research facilities') AS ref_vec_0,\n\nsimilar_classrooms AS (\n SELECT building, room_number, distance(classroom.classroom_description_embedding, ref_vec_0) AS distance FROM classroom\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.name FROM instructor i JOIN teaches t ON toString(i.ID) = toString(t.ID) JOIN section s ON toString(t.course_id) = toString(s.course_id) AND t.sec_id = s.sec_id AND t.semester = s.semester AND t.year = s.year JOIN similar_classrooms sc ON toString(s.building) = toString(sc.building) AND s.room_number = sc.room_number ORDER BY sc.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 53, server response: Code: 53. DB::Exception: Can't infer common type for joined columns: _--t.year: Nullable(Decimal(38, 6)) at left, _--s.year: Nullable(Float64) at right. There is no supertype for types Decimal(38, 6), Float64 because some of them have no lossless conversion to Decimal. (TYPE_MISMATCH) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);" + }, + { + "db_id": "flight_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'American airline operating in the USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aberdeen Airport in the United States') AS ref_vec_1,\n\nairlines_filtered AS (\n SELECT\n *,\n distance(airlines_description_embedding, ref_vec_0) AS distance\n FROM airlines\n\n ORDER BY distance\n LIMIT 5\n),\n\nairports_filtered AS (\n SELECT\n *,\n distance(airports_description_embedding, ref_vec_1) AS distance\n FROM airports\n\n ORDER BY distance\n LIMIT 5\n),\n\nAirlineMatches AS (\n SELECT uid, Airline, distance\n FROM airlines_filtered AS airlines BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance\n FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAirport\nFROM flights f\nJOIN AirlineMatches am ON toString(f.Airline) = toString(am.uid)\nJOIN AirportMatches apm ON toString(f.SourceAirport) = toString(apm.AirportCode)\nORDER BY f.FlightNo, apm.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Soar through the skies and discover the flight numbers and departing airports of journeys operated by the top five American airlines and originating from the premier gateways akin to Aberdeen Airport in the USA. Reveal the first 10 flights that fit this narrative.", + "external_knowledge": "The `MATCH` operation in the query performs an approximate nearest neighbor (ANN) search using vector embeddings. This technique is crucial in finding entries that closely resemble the given description in semantic space. The `k=5` parameter indicates that the query retrieves the top 5 entries most similar to the provided descriptions. These vector operations utilize models like `all-MiniLM-L6-v2` to compute embeddings that encapsulate semantic meanings, allowing for efficient comparison and ranking based on Euclidean distance. The closer the distance, the more similar the items are considered within this vector space.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top five US airlines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major US airports similar to Aberdeen') AS ref_vec_1,\n\nairlines_filtered AS (\n SELECT\n *,\n distance(airlines_description_embedding, ref_vec_0) AS distance\n FROM airlines\n\n ORDER BY distance\n LIMIT 5\n),\n\nairports_filtered AS (\n SELECT\n *,\n distance(airports_description_embedding, ref_vec_1) AS distance\n FROM airports\n\n ORDER BY distance\n LIMIT 5\n),\n\nAirlineMatches AS (\n SELECT uid, Airline, distance FROM airlines_filtered AS airlines BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAirport FROM flights f JOIN AirlineMatches am ON toString(f.Airline) = toString(am.uid) JOIN AirportMatches apm ON toString(f.SourceAirport) = toString(apm.AirportCode) ORDER BY f.FlightNo, apm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading American airlines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'US airports comparable to Aberdeen') AS ref_vec_1,\n\nairlines_filtered AS (\n SELECT\n *,\n distance(airlines_description_embedding, ref_vec_0) AS distance\n FROM airlines\n\n ORDER BY distance\n LIMIT 5\n),\n\nairports_filtered AS (\n SELECT\n *,\n distance(airports_description_embedding, ref_vec_1) AS distance\n FROM airports\n\n ORDER BY distance\n LIMIT 5\n),\n\nAirlineMatches AS (\n SELECT uid, Airline, distance FROM airlines_filtered AS airlines BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAirport FROM flights f JOIN AirlineMatches am ON toString(f.Airline) = toString(am.uid) JOIN AirportMatches apm ON toString(f.SourceAirport) = toString(apm.AirportCode) ORDER BY f.FlightNo, apm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top American carriers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Key US airports like Aberdeen') AS ref_vec_1,\n\nairlines_filtered AS (\n SELECT\n *,\n distance(airlines_description_embedding, ref_vec_0) AS distance\n FROM airlines\n\n ORDER BY distance\n LIMIT 5\n),\n\nairports_filtered AS (\n SELECT\n *,\n distance(airports_description_embedding, ref_vec_1) AS distance\n FROM airports\n\n ORDER BY distance\n LIMIT 5\n),\n\nAirlineMatches AS (\n SELECT uid, Airline, distance FROM airlines_filtered AS airlines BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAirport FROM flights f JOIN AirlineMatches am ON toString(f.Airline) = toString(am.uid) JOIN AirportMatches apm ON toString(f.SourceAirport) = toString(apm.AirportCode) ORDER BY f.FlightNo, apm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Premier airlines in the USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'US gateways similar to Aberdeen') AS ref_vec_1,\n\nairlines_filtered AS (\n SELECT\n *,\n distance(airlines_description_embedding, ref_vec_0) AS distance\n FROM airlines\n\n ORDER BY distance\n LIMIT 5\n),\n\nairports_filtered AS (\n SELECT\n *,\n distance(airports_description_embedding, ref_vec_1) AS distance\n FROM airports\n\n ORDER BY distance\n LIMIT 5\n),\n\nAirlineMatches AS (\n SELECT uid, Airline, distance FROM airlines_filtered AS airlines BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAirport FROM flights f JOIN AirlineMatches am ON toString(f.Airline) = toString(am.uid) JOIN AirportMatches apm ON toString(f.SourceAirport) = toString(apm.AirportCode) ORDER BY f.FlightNo, apm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top 5 American airlines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major US airports akin to Aberdeen') AS ref_vec_1,\n\nairlines_filtered AS (\n SELECT\n *,\n distance(airlines_description_embedding, ref_vec_0) AS distance\n FROM airlines\n\n ORDER BY distance\n LIMIT 5\n),\n\nairports_filtered AS (\n SELECT\n *,\n distance(airports_description_embedding, ref_vec_1) AS distance\n FROM airports\n\n ORDER BY distance\n LIMIT 5\n),\n\nAirlineMatches AS (\n SELECT uid, Airline, distance FROM airlines_filtered AS airlines BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAirport FROM flights f JOIN AirlineMatches am ON toString(f.Airline) = toString(am.uid) JOIN AirportMatches apm ON toString(f.SourceAirport) = toString(apm.AirportCode) ORDER BY f.FlightNo, apm.distance LIMIT 10;" + ], + "integration_level": 7, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17331 ('BY') (line 27, col 42): BY distance\n),\n\nAirportMatches AS (\n SELECT City, AirportCode, distance\n FROM airports_filtered AS airports BY distance\n)\n\nSELECT f.FlightNo, f.SourceAi. Expected one of: FINAL, SAMPLE, table, table function, subquery or list of joined tables, array join, LEFT ARRAY JOIN, INNER, ARRAY JOIN, GLOBAL, LOCAL, ANY, ALL, ASOF, SEMI, ANTI, ONLY, LEFT, RIGHT, FULL, CROSS, PASTE, JOIN, PREWHERE, WHERE, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE airlines (\n `uid` Nullable(Int64),\n `Airline` Nullable(String),\n `Abbreviation` Nullable(String),\n `Country` Nullable(String),\n `airlines_description` Nullable(String),\n `airlines_description_embedding` Array(Float32)\n);\nCREATE TABLE airports (\n `City` Nullable(String),\n `AirportCode` Nullable(String),\n `AirportName` Nullable(String),\n `Country` Nullable(String),\n `CountryAbbrev` Nullable(String),\n `airports_description` Nullable(String),\n `airports_description_embedding` Array(Float32)\n);\nCREATE TABLE flights (\n `Airline` Nullable(Int64),\n `FlightNo` Nullable(Int64),\n `SourceAirport` Nullable(String),\n `DestAirport` Nullable(String)\n);" + }, + { + "db_id": "hospital_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Reverse Rhinopodoplasty') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cardiology department') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(Procedures_description_embedding, ref_vec_0) AS distance\n FROM Procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nd_filtered AS (\n SELECT\n *,\n distance(Department_description_embedding, ref_vec_1) AS distance\n FROM Department\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.Name\nFROM Physician p\nJOIN Trained_In t ON toString(p.EmployeeID) = toString(t.Physician)\nJOIN pr_filtered AS pr ON toString(t.Treatment) = toString(pr.Code)\nJOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician)\nJOIN d_filtered AS d ON toString(aw.Department) = toString(d.DepartmentID)\n WHERE aw.PrimaryAffiliation = 1 ORDER BY pr.distance, d.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the names of the top 3 physicians who are primarily affiliated with a department closely related to cardiology and are trained in procedures that relate to reverse rhinopodoplasty?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Reverse Rhinopodoplasty techniques') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cardiac care department') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(Procedures_description_embedding, ref_vec_0) AS distance\n FROM Procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nd_filtered AS (\n SELECT\n *,\n distance(Department_description_embedding, ref_vec_1) AS distance\n FROM Department\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.Name FROM Physician p JOIN Trained_In t ON toString(p.EmployeeID) = toString(t.Physician) JOIN pr_filtered AS pr ON toString(t.Treatment) = toString(pr.Code) JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN d_filtered AS d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1 ORDER BY pr.distance, d.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rhinopodoplasty reversal') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Heart department') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(Procedures_description_embedding, ref_vec_0) AS distance\n FROM Procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nd_filtered AS (\n SELECT\n *,\n distance(Department_description_embedding, ref_vec_1) AS distance\n FROM Department\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.Name FROM Physician p JOIN Trained_In t ON toString(p.EmployeeID) = toString(t.Physician) JOIN pr_filtered AS pr ON toString(t.Treatment) = toString(pr.Code) JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN d_filtered AS d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1 ORDER BY pr.distance, d.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Procedures for reverse rhinopodoplasty') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cardiological department') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(Procedures_description_embedding, ref_vec_0) AS distance\n FROM Procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nd_filtered AS (\n SELECT\n *,\n distance(Department_description_embedding, ref_vec_1) AS distance\n FROM Department\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.Name FROM Physician p JOIN Trained_In t ON toString(p.EmployeeID) = toString(t.Physician) JOIN pr_filtered AS pr ON toString(t.Treatment) = toString(pr.Code) JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN d_filtered AS d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1 ORDER BY pr.distance, d.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Reversal of rhinopodoplasty') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Department related to cardiology') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(Procedures_description_embedding, ref_vec_0) AS distance\n FROM Procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nd_filtered AS (\n SELECT\n *,\n distance(Department_description_embedding, ref_vec_1) AS distance\n FROM Department\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.Name FROM Physician p JOIN Trained_In t ON toString(p.EmployeeID) = toString(t.Physician) JOIN pr_filtered AS pr ON toString(t.Treatment) = toString(pr.Code) JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN d_filtered AS d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1 ORDER BY pr.distance, d.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Reverse rhinopodoplasty operations') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cardiology related department') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(Procedures_description_embedding, ref_vec_0) AS distance\n FROM Procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nd_filtered AS (\n SELECT\n *,\n distance(Department_description_embedding, ref_vec_1) AS distance\n FROM Department\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.Name FROM Physician p JOIN Trained_In t ON toString(p.EmployeeID) = toString(t.Physician) JOIN pr_filtered AS pr ON toString(t.Treatment) = toString(pr.Code) JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN d_filtered AS d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1 ORDER BY pr.distance, d.distance;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 386, server response: Code: 386. DB::Exception: There is no supertype for types String, UInt8 because some of them are String/FixedString/Enum and some of them are not: while executing 'FUNCTION equals(PrimaryAffiliation : 9, 1 : 10) -> equals(PrimaryAffiliation, 1) UInt8 : 11'. (NO_COMMON_TYPE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Affiliated_With (\n `Physician` Int64,\n `Department` Int64,\n `PrimaryAffiliation` String\n);\nCREATE TABLE Appointment (\n `AppointmentID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `PrepNurse` Nullable(Int64),\n `Physician` Nullable(Int64),\n `Start` Nullable(String),\n `End` Nullable(String),\n `ExaminationRoom` Nullable(String),\n `Appointment_description` Nullable(String),\n `Appointment_description_embedding` Array(Float32)\n);\nCREATE TABLE Block (\n `BlockFloor` Int64,\n `BlockCode` Int64\n);\nCREATE TABLE Department (\n `DepartmentID` Nullable(Int64),\n `Name` Nullable(String),\n `Head` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Medication (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Brand` Nullable(String),\n `Description` Nullable(String),\n `Medication_description` Nullable(String),\n `Medication_description_embedding` Array(Float32)\n);\nCREATE TABLE Nurse (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `Registered` Nullable(String),\n `SSN` Nullable(Int64),\n `Nurse_description` Nullable(String),\n `Nurse_description_embedding` Array(Float32)\n);\nCREATE TABLE On_Call (\n `Nurse` Int64,\n `BlockFloor` Int64,\n `BlockCode` Int64,\n `OnCallStart` Date,\n `OnCallEnd` Date\n);\nCREATE TABLE Patient (\n `SSN` Nullable(Int64),\n `Name` Nullable(String),\n `Address` Nullable(String),\n `Phone` Nullable(String),\n `InsuranceID` Nullable(Int64),\n `PCP` Nullable(Int64),\n `Patient_description` Nullable(String),\n `Patient_description_embedding` Array(Float32)\n);\nCREATE TABLE Physician (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `SSN` Nullable(Int64),\n `Physician_description` Nullable(String),\n `Physician_description_embedding` Array(Float32)\n);\nCREATE TABLE Prescribes (\n `Physician` Int64,\n `Patient` Int64,\n `Medication` Int64,\n `Date` Date,\n `Appointment` Nullable(Int64),\n `Dose` String\n);\nCREATE TABLE Procedures (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Cost` Nullable(Float64),\n `Procedures_description` Nullable(String),\n `Procedures_description_embedding` Array(Float32)\n);\nCREATE TABLE Room (\n `RoomNumber` Nullable(Int64),\n `RoomType` Nullable(String),\n `BlockFloor` Nullable(Int64),\n `BlockCode` Nullable(Int64),\n `Unavailable` Nullable(String),\n `Room_description` Nullable(String),\n `Room_description_embedding` Array(Float32)\n);\nCREATE TABLE Stay (\n `StayID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `Room` Nullable(Int64),\n `StayStart` Nullable(String),\n `StayEnd` Nullable(String),\n `Stay_description` Nullable(String),\n `Stay_description_embedding` Array(Float32)\n);\nCREATE TABLE Trained_In (\n `Physician` Int64,\n `Treatment` Int64,\n `CertificationDate` Date,\n `CertificationExpires` Date\n);\nCREATE TABLE Undergoes (\n `Patient` Int64,\n `Procedures` Int64,\n `Stay` Int64,\n `DateUndergoes` Date,\n `Physician` Int64,\n `AssistingNurse` Nullable(Int64)\n);" + }, + { + "db_id": "college_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A student in the Computer Science department.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the name of the student who best matches the description of being in the Computer Science department?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A Computer Science department student.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Student belonging to Computer Science.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Enrolled in Computer Science department.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Computer Science student.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A learner in the Computer Science field.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);" + }, + { + "db_id": "college_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Room with advanced multimedia capabilities suitable for lectures.') AS ref_vec_0\n\nSELECT c.classroom_description, distance(c.classroom_description_embedding, ref_vec_0) AS distance\nFROM classroom c\nJOIN section s ON toString(c.building) = toString(s.building) AND c.room_number = s.room_number\nJOIN teaches t ON toString(s.course_id) = toString(t.course_id) AND s.sec_id = t.sec_id AND s.semester = t.semester AND s.year = t.year\nJOIN instructor i ON toString(t.ID) = toString(i.ID)\nJOIN department d ON toString(i.dept_name) = toString(d.dept_name)\nWHERE s.semester = 'Fall'\n AND s.year = 2023\n AND d.dept_name = 'Computer Science'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Could you find me the top 5 classrooms that have advanced multimedia capabilities good for lectures, used by the Computer Science department in Fall 2023? I need their descriptions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Classrooms equipped with state-of-the-art multimedia facilities for teaching.') AS ref_vec_0\n\nSELECT c.classroom_description, distance(c.classroom_description_embedding, ref_vec_0) AS distance FROM classroom c JOIN section s ON toString(c.building) = toString(s.building) AND c.room_number = s.room_number JOIN teaches t ON toString(s.course_id) = toString(t.course_id) AND s.sec_id = t.sec_id AND s.semester = t.semester AND s.year = t.year JOIN instructor i ON toString(t.ID) = toString(i.ID) JOIN department d ON toString(i.dept_name) = toString(d.dept_name) WHERE s.semester = 'Fall' AND s.year = 2023 AND d.dept_name = 'Computer Science'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Lecture halls with comprehensive multimedia systems for academic use.') AS ref_vec_0\n\nSELECT c.classroom_description, distance(c.classroom_description_embedding, ref_vec_0) AS distance FROM classroom c JOIN section s ON toString(c.building) = toString(s.building) AND c.room_number = s.room_number JOIN teaches t ON toString(s.course_id) = toString(t.course_id) AND s.sec_id = t.sec_id AND s.semester = t.semester AND s.year = t.year JOIN instructor i ON toString(t.ID) = toString(i.ID) JOIN department d ON toString(i.dept_name) = toString(d.dept_name) WHERE s.semester = 'Fall' AND s.year = 2023 AND d.dept_name = 'Computer Science'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Spaces featuring advanced audio-visual technology for educational purposes.') AS ref_vec_0\n\nSELECT c.classroom_description, distance(c.classroom_description_embedding, ref_vec_0) AS distance FROM classroom c JOIN section s ON toString(c.building) = toString(s.building) AND c.room_number = s.room_number JOIN teaches t ON toString(s.course_id) = toString(t.course_id) AND s.sec_id = t.sec_id AND s.semester = t.semester AND s.year = t.year JOIN instructor i ON toString(t.ID) = toString(i.ID) JOIN department d ON toString(i.dept_name) = toString(d.dept_name) WHERE s.semester = 'Fall' AND s.year = 2023 AND d.dept_name = 'Computer Science'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rooms with high-tech multimedia equipment ideal for lectures.') AS ref_vec_0\n\nSELECT c.classroom_description, distance(c.classroom_description_embedding, ref_vec_0) AS distance FROM classroom c JOIN section s ON toString(c.building) = toString(s.building) AND c.room_number = s.room_number JOIN teaches t ON toString(s.course_id) = toString(t.course_id) AND s.sec_id = t.sec_id AND s.semester = t.semester AND s.year = t.year JOIN instructor i ON toString(t.ID) = toString(i.ID) JOIN department d ON toString(i.dept_name) = toString(d.dept_name) WHERE s.semester = 'Fall' AND s.year = 2023 AND d.dept_name = 'Computer Science'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Educational spaces with enhanced multimedia features for lectures.') AS ref_vec_0\n\nSELECT c.classroom_description, distance(c.classroom_description_embedding, ref_vec_0) AS distance FROM classroom c JOIN section s ON toString(c.building) = toString(s.building) AND c.room_number = s.room_number JOIN teaches t ON toString(s.course_id) = toString(t.course_id) AND s.sec_id = t.sec_id AND s.semester = t.semester AND s.year = t.year JOIN instructor i ON toString(t.ID) = toString(i.ID) JOIN department d ON toString(i.dept_name) = toString(d.dept_name) WHERE s.semester = 'Fall' AND s.year = 2023 AND d.dept_name = 'Computer Science'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 53, server response: Code: 53. DB::Exception: Can't infer common type for joined columns: _--s.year: Nullable(Float64) at left, _--t.year: Nullable(Decimal(38, 6)) at right. There is no supertype for types Float64, Decimal(38, 6) because some of them have no lossless conversion to Decimal. (TYPE_MISMATCH) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);" + }, + { + "db_id": "college_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An introductory course in programming with a focus on C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance\nFROM course\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need to find the course title of the introductory programming course that focuses on the C language. Can you provide the best match for this in terms of course description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Introductory programming course centered around C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Beginner programming class with emphasis on C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Programming fundamentals course focusing on C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Intro to programming using C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Basic programming course with C language focus.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);" + }, + { + "db_id": "book_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Black Sheep in a modern setting') AS ref_vec_0\n\nSELECT \n Book_ID, \n Title, distance(book.Title_embedding, ref_vec_0) AS distance\nFROM \n book\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Can you uncover the book that embodies the essence of a trailblazer, akin to a modern-day 'Black Sheep'?", + "external_knowledge": "The `MATCH` operator is used in vector searches to find items that are similar to a given vector representation, based on the concept of approximate nearest neighbor (ANN) search. In this context, vector embeddings are numerical representations of text used to compare semantic similarity. The model 'all-MiniLM-L6-v2' is employed for generating these embeddings, which allows for capturing intricate patterns in language. The phrase \"The Black Sheep in a modern setting\" serves as a metaphor, suggesting a book that depicts nonconformity or uniqueness against contemporary norms.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A pioneering spirit in contemporary literature') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The essence of a modern trailblazer') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An innovative thinker in today’s world') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A contemporary rebel with visionary ideas') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A groundbreaking figure in modern storytelling') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE book (\n `Book_ID` Nullable(Int64),\n `Title` Nullable(String),\n `Issues` Nullable(Float64),\n `Writer` Nullable(String),\n `book_description` Nullable(String),\n `Title_embedding` Array(Float32),\n `book_description_embedding` Array(Float32)\n);\nCREATE TABLE publication (\n `Publication_ID` Nullable(Int64),\n `Book_ID` Nullable(Int64),\n `Publisher` Nullable(String),\n `Publication_Date` Nullable(String),\n `Price` Nullable(Float64)\n);" + }, + { + "db_id": "hospital_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Jane Doe lives at 123 Elm Street and can be reached at 555-1234. Her insurance ID is 98765432 and her primary care physician is assigned ID 2.') AS ref_vec_0,\n\nExpiringCertifications AS (\n SELECT Physician, CertificationExpires\n FROM Trained_In\n WHERE CertificationExpires BETWEEN now() AND date_add(DAY, 30, now())\n),\n\nSimilarPatients AS (\n SELECT SSN, distance(Patient.Patient_description_embedding, ref_vec_0) AS distance\n FROM Patient\n ORDER BY distance\n LIMIT 1\n),\n\nFilteredPhysicians AS (\n SELECT p.EmployeeID, p.Name, aw.Department, ec.CertificationExpires\n FROM Physician p\n JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) AND aw.PrimaryAffiliation = 1\n JOIN ExpiringCertifications ec ON toString(p.EmployeeID) = toString(ec.Physician)\n)\n\nSELECT fp.Name\nFROM FilteredPhysicians fp\nJOIN SimilarPatients sp ON toString(fp.EmployeeID) = toString(sp.SSN)\nORDER BY sp.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who is the physician with an expiring certification within the next month and primarily affiliated with a department, linked to the patient most resembling Jane Doe's description?", + "external_knowledge": "The `MATCH` operator in the vector search performs approximate nearest neighbor (ANN) operations to identify the closest vector matches. The `lembed` function uses embeddings to compare the semantic similarity of text descriptions. In this context, \"Jane Doe lives at 123 Elm Street and can be reached at 555-1234. Her insurance ID is 98765432 and her primary care physician is assigned ID 2.\" is transformed into a vector, and patient records are searched for the closest match based on Euclidean distance. The distance metric signifies similarity, with smaller distances indicating higher similarity. Thus, the query identifies the patient whose description is most similar to that vector representation.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Jane Doe, residing at 123 Elm Street, contact number 555-1234, insurance ID 98765432, primary care physician ID 2.') AS ref_vec_0,\n\nExpiringCertifications AS (\n SELECT Physician, CertificationExpires FROM Trained_In WHERE CertificationExpires BETWEEN now() AND date_add(DAY, 30, now())\n),\n\nSimilarPatients AS (\n SELECT SSN, distance(Patient.Patient_description_embedding, ref_vec_0) AS distance FROM Patient\n ORDER BY distance\n LIMIT 1\n),\n\nFilteredPhysicians AS (\n SELECT p.EmployeeID, p.Name, aw.Department, ec.CertificationExpires FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) AND aw.PrimaryAffiliation = 1 JOIN ExpiringCertifications ec ON toString(p.EmployeeID) = toString(ec.Physician)\n)\n\nSELECT fp.Name FROM FilteredPhysicians fp JOIN SimilarPatients sp ON toString(fp.EmployeeID) = toString(sp.SSN) ORDER BY sp.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Patient Jane Doe, living at 123 Elm Street, phone 555-1234, insurance ID 98765432, primary care doctor ID 2.') AS ref_vec_0,\n\nExpiringCertifications AS (\n SELECT Physician, CertificationExpires FROM Trained_In WHERE CertificationExpires BETWEEN now() AND date_add(DAY, 30, now())\n),\n\nSimilarPatients AS (\n SELECT SSN, distance(Patient.Patient_description_embedding, ref_vec_0) AS distance FROM Patient\n ORDER BY distance\n LIMIT 1\n),\n\nFilteredPhysicians AS (\n SELECT p.EmployeeID, p.Name, aw.Department, ec.CertificationExpires FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) AND aw.PrimaryAffiliation = 1 JOIN ExpiringCertifications ec ON toString(p.EmployeeID) = toString(ec.Physician)\n)\n\nSELECT fp.Name FROM FilteredPhysicians fp JOIN SimilarPatients sp ON toString(fp.EmployeeID) = toString(sp.SSN) ORDER BY sp.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Jane Doe, from 123 Elm Street, reachable at 555-1234, insurance number 98765432, primary doctor ID 2.') AS ref_vec_0,\n\nExpiringCertifications AS (\n SELECT Physician, CertificationExpires FROM Trained_In WHERE CertificationExpires BETWEEN now() AND date_add(DAY, 30, now())\n),\n\nSimilarPatients AS (\n SELECT SSN, distance(Patient.Patient_description_embedding, ref_vec_0) AS distance FROM Patient\n ORDER BY distance\n LIMIT 1\n),\n\nFilteredPhysicians AS (\n SELECT p.EmployeeID, p.Name, aw.Department, ec.CertificationExpires FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) AND aw.PrimaryAffiliation = 1 JOIN ExpiringCertifications ec ON toString(p.EmployeeID) = toString(ec.Physician)\n)\n\nSELECT fp.Name FROM FilteredPhysicians fp JOIN SimilarPatients sp ON toString(fp.EmployeeID) = toString(sp.SSN) ORDER BY sp.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Jane Doe, address 123 Elm Street, phone number 555-1234, insurance ID 98765432, physician ID 2.') AS ref_vec_0,\n\nExpiringCertifications AS (\n SELECT Physician, CertificationExpires FROM Trained_In WHERE CertificationExpires BETWEEN now() AND date_add(DAY, 30, now())\n),\n\nSimilarPatients AS (\n SELECT SSN, distance(Patient.Patient_description_embedding, ref_vec_0) AS distance FROM Patient\n ORDER BY distance\n LIMIT 1\n),\n\nFilteredPhysicians AS (\n SELECT p.EmployeeID, p.Name, aw.Department, ec.CertificationExpires FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) AND aw.PrimaryAffiliation = 1 JOIN ExpiringCertifications ec ON toString(p.EmployeeID) = toString(ec.Physician)\n)\n\nSELECT fp.Name FROM FilteredPhysicians fp JOIN SimilarPatients sp ON toString(fp.EmployeeID) = toString(sp.SSN) ORDER BY sp.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Jane Doe residing at 123 Elm Street, phone 555-1234, insurance ID 98765432, doctor ID 2.') AS ref_vec_0,\n\nExpiringCertifications AS (\n SELECT Physician, CertificationExpires FROM Trained_In WHERE CertificationExpires BETWEEN now() AND date_add(DAY, 30, now())\n),\n\nSimilarPatients AS (\n SELECT SSN, distance(Patient.Patient_description_embedding, ref_vec_0) AS distance FROM Patient\n ORDER BY distance\n LIMIT 1\n),\n\nFilteredPhysicians AS (\n SELECT p.EmployeeID, p.Name, aw.Department, ec.CertificationExpires FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) AND aw.PrimaryAffiliation = 1 JOIN ExpiringCertifications ec ON toString(p.EmployeeID) = toString(ec.Physician)\n)\n\nSELECT fp.Name FROM FilteredPhysicians fp JOIN SimilarPatients sp ON toString(fp.EmployeeID) = toString(sp.SSN) ORDER BY sp.distance LIMIT 1;" + ], + "integration_level": 2, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 386, server response: Code: 386. DB::Exception: There is no supertype for types String, UInt8 because some of them are String/FixedString/Enum and some of them are not. (NO_COMMON_TYPE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Affiliated_With (\n `Physician` Int64,\n `Department` Int64,\n `PrimaryAffiliation` String\n);\nCREATE TABLE Appointment (\n `AppointmentID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `PrepNurse` Nullable(Int64),\n `Physician` Nullable(Int64),\n `Start` Nullable(String),\n `End` Nullable(String),\n `ExaminationRoom` Nullable(String),\n `Appointment_description` Nullable(String),\n `Appointment_description_embedding` Array(Float32)\n);\nCREATE TABLE Block (\n `BlockFloor` Int64,\n `BlockCode` Int64\n);\nCREATE TABLE Department (\n `DepartmentID` Nullable(Int64),\n `Name` Nullable(String),\n `Head` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Medication (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Brand` Nullable(String),\n `Description` Nullable(String),\n `Medication_description` Nullable(String),\n `Medication_description_embedding` Array(Float32)\n);\nCREATE TABLE Nurse (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `Registered` Nullable(String),\n `SSN` Nullable(Int64),\n `Nurse_description` Nullable(String),\n `Nurse_description_embedding` Array(Float32)\n);\nCREATE TABLE On_Call (\n `Nurse` Int64,\n `BlockFloor` Int64,\n `BlockCode` Int64,\n `OnCallStart` Date,\n `OnCallEnd` Date\n);\nCREATE TABLE Patient (\n `SSN` Nullable(Int64),\n `Name` Nullable(String),\n `Address` Nullable(String),\n `Phone` Nullable(String),\n `InsuranceID` Nullable(Int64),\n `PCP` Nullable(Int64),\n `Patient_description` Nullable(String),\n `Patient_description_embedding` Array(Float32)\n);\nCREATE TABLE Physician (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `SSN` Nullable(Int64),\n `Physician_description` Nullable(String),\n `Physician_description_embedding` Array(Float32)\n);\nCREATE TABLE Prescribes (\n `Physician` Int64,\n `Patient` Int64,\n `Medication` Int64,\n `Date` Date,\n `Appointment` Nullable(Int64),\n `Dose` String\n);\nCREATE TABLE Procedures (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Cost` Nullable(Float64),\n `Procedures_description` Nullable(String),\n `Procedures_description_embedding` Array(Float32)\n);\nCREATE TABLE Room (\n `RoomNumber` Nullable(Int64),\n `RoomType` Nullable(String),\n `BlockFloor` Nullable(Int64),\n `BlockCode` Nullable(Int64),\n `Unavailable` Nullable(String),\n `Room_description` Nullable(String),\n `Room_description_embedding` Array(Float32)\n);\nCREATE TABLE Stay (\n `StayID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `Room` Nullable(Int64),\n `StayStart` Nullable(String),\n `StayEnd` Nullable(String),\n `Stay_description` Nullable(String),\n `Stay_description_embedding` Array(Float32)\n);\nCREATE TABLE Trained_In (\n `Physician` Int64,\n `Treatment` Int64,\n `CertificationDate` Date,\n `CertificationExpires` Date\n);\nCREATE TABLE Undergoes (\n `Patient` Int64,\n `Procedures` Int64,\n `Stay` Int64,\n `DateUndergoes` Date,\n `Physician` Int64,\n `AssistingNurse` Nullable(Int64)\n);" + }, + { + "db_id": "hospital_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, a renowned cardiologist known for his expertise in heart disease management and surgical interventions.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance\n FROM Physician p\n JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName\n FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName\nFROM PhysicianAffiliation pa\nJOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID)\nORDER BY pa.PrimaryAffiliation DESC;", + "sql_result_column_count": 2, + "sql_result_rows_count": 11, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 10 doctors who are like Dr. John Smith, the awesome heart expert, and tell me what departments they're in? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, an exceptional cardiologist specializing in heart health and surgical treatments.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, a leading expert in cardiology and heart surgery.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, famous for his heart disease expertise and surgical skills.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, renowned for his proficiency in managing heart conditions and performing surgeries.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, a top cardiologist with expertise in heart disease management and surgery.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Affiliated_With (\n `Physician` Int64,\n `Department` Int64,\n `PrimaryAffiliation` String\n);\nCREATE TABLE Appointment (\n `AppointmentID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `PrepNurse` Nullable(Int64),\n `Physician` Nullable(Int64),\n `Start` Nullable(String),\n `End` Nullable(String),\n `ExaminationRoom` Nullable(String),\n `Appointment_description` Nullable(String),\n `Appointment_description_embedding` Array(Float32)\n);\nCREATE TABLE Block (\n `BlockFloor` Int64,\n `BlockCode` Int64\n);\nCREATE TABLE Department (\n `DepartmentID` Nullable(Int64),\n `Name` Nullable(String),\n `Head` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Medication (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Brand` Nullable(String),\n `Description` Nullable(String),\n `Medication_description` Nullable(String),\n `Medication_description_embedding` Array(Float32)\n);\nCREATE TABLE Nurse (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `Registered` Nullable(String),\n `SSN` Nullable(Int64),\n `Nurse_description` Nullable(String),\n `Nurse_description_embedding` Array(Float32)\n);\nCREATE TABLE On_Call (\n `Nurse` Int64,\n `BlockFloor` Int64,\n `BlockCode` Int64,\n `OnCallStart` Date,\n `OnCallEnd` Date\n);\nCREATE TABLE Patient (\n `SSN` Nullable(Int64),\n `Name` Nullable(String),\n `Address` Nullable(String),\n `Phone` Nullable(String),\n `InsuranceID` Nullable(Int64),\n `PCP` Nullable(Int64),\n `Patient_description` Nullable(String),\n `Patient_description_embedding` Array(Float32)\n);\nCREATE TABLE Physician (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `SSN` Nullable(Int64),\n `Physician_description` Nullable(String),\n `Physician_description_embedding` Array(Float32)\n);\nCREATE TABLE Prescribes (\n `Physician` Int64,\n `Patient` Int64,\n `Medication` Int64,\n `Date` Date,\n `Appointment` Nullable(Int64),\n `Dose` String\n);\nCREATE TABLE Procedures (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Cost` Nullable(Float64),\n `Procedures_description` Nullable(String),\n `Procedures_description_embedding` Array(Float32)\n);\nCREATE TABLE Room (\n `RoomNumber` Nullable(Int64),\n `RoomType` Nullable(String),\n `BlockFloor` Nullable(Int64),\n `BlockCode` Nullable(Int64),\n `Unavailable` Nullable(String),\n `Room_description` Nullable(String),\n `Room_description_embedding` Array(Float32)\n);\nCREATE TABLE Stay (\n `StayID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `Room` Nullable(Int64),\n `StayStart` Nullable(String),\n `StayEnd` Nullable(String),\n `Stay_description` Nullable(String),\n `Stay_description_embedding` Array(Float32)\n);\nCREATE TABLE Trained_In (\n `Physician` Int64,\n `Treatment` Int64,\n `CertificationDate` Date,\n `CertificationExpires` Date\n);\nCREATE TABLE Undergoes (\n `Patient` Int64,\n `Procedures` Int64,\n `Stay` Int64,\n `DateUndergoes` Date,\n `Physician` Int64,\n `AssistingNurse` Nullable(Int64)\n);" + }, + { + "db_id": "icfp_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring techniques for simplifying monadic equational reasoning') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance\nFROM Papers\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you help me out by finding the paper ID for the most relevant paper that talks about simplifying monadic equational reasoning?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Simplification methods in monadic equational logic') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Approaches to streamline monadic equational reasoning') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Monadic equational reasoning simplification strategies') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Techniques for simplifying reasoning in monadic equations') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Efficient methods for monadic equational reasoning simplification') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Authors (\n `authID` Nullable(Int64),\n `lname` Nullable(String),\n `fname` Nullable(String),\n `Authors_description` Nullable(String),\n `Authors_description_embedding` Array(Float32)\n);\nCREATE TABLE Authorship (\n `authID` Nullable(Int64),\n `instID` Nullable(Int64),\n `paperID` Nullable(Int64),\n `authOrder` Nullable(Int64)\n);\nCREATE TABLE Inst (\n `instID` Nullable(Int64),\n `name` Nullable(String),\n `country` Nullable(String),\n `Inst_description` Nullable(String),\n `Inst_description_embedding` Array(Float32)\n);\nCREATE TABLE Papers (\n `paperID` Nullable(Int64),\n `title` Nullable(String),\n `Papers_description` Nullable(String),\n `title_embedding` Array(Float32),\n `Papers_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "network_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John is a software engineer living in San Francisco who enjoys hiking and playing the guitar.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance\n FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name\nFROM SimilarPersons SP\nJOIN PersonFriend PF ON toString(SP.name) = toString(PF.name)\nWHERE PF.year > 2015\nORDER BY SP.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who are the people who share similarities with John, especially in terms of interests and profession, and have formed friendships that started after 2015?", + "external_knowledge": "In vector operations, the `MATCH` operator is used to perform approximate nearest neighbor (ANN) searches with embeddings to identify items that are most similar to a given query. The `LIMIT` clause specifies how many similar items to return—in this case, the top 5. The `lembed` function generates vector embeddings based on text descriptions, which are then compared using Euclidean distance (L2 norm) by default. Greater similarity corresponds to smaller distance values. \"John is a software engineer living in San Francisco who enjoys hiking and playing the guitar\" is a description used to find other people with similar traits or activities, interpreted through the vector embeddings.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'John is a tech professional in Silicon Valley who loves outdoor activities and music.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John works in software development and is passionate about hiking and guitar playing.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John is a software engineer who enjoys outdoor sports and music hobbies.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John is an IT professional interested in hiking and playing musical instruments.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John is a tech worker who loves exploring nature and playing the guitar.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Person (\n `name` Nullable(String),\n `age` Nullable(Int64),\n `city` Nullable(String),\n `gender` Nullable(String),\n `job` Nullable(String),\n `Person_description` Nullable(String),\n `Person_description_embedding` Array(Float32)\n);\nCREATE TABLE PersonFriend (\n `name` Nullable(String),\n `friend` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE Person_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "hospital_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'specialized cardiology department with leading research facilities') AS ref_vec_0\n\nSELECT p.Physician_description, distance(d.Department_description_embedding, ref_vec_0) AS distance\nFROM Physician p\nJOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician)\nJOIN Department d ON toString(aw.Department) = toString(d.DepartmentID)\nWHERE aw.PrimaryAffiliation = 1\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 9, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Can you find the physicians who navigate the corridors of a specialized cardiology department renowned for its pioneering research, and tell me their stories?", + "external_knowledge": "The `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search to find departments that are most semantically similar to the description provided (\"specialized cardiology department with leading research facilities\"). This operation uses vector embeddings to compare the semantic content of department descriptions, leveraging Euclidean distance (L2 norm) to measure similarity. The search aims to return the top 3 departments (`k = 3`) that align closely with the specified metaphorical description, thereby identifying leading research facilities in cardiology. In this context, the metaphorical expression \"navigate the corridors\" implies working within or being affiliated with the department.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'cardiology specialists in an innovative research environment') AS ref_vec_0\n\nSELECT p.Physician_description, distance(d.Department_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN Department d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'leading-edge cardiology department known for research breakthroughs') AS ref_vec_0\n\nSELECT p.Physician_description, distance(d.Department_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN Department d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'renowned cardiology research department with expert physicians') AS ref_vec_0\n\nSELECT p.Physician_description, distance(d.Department_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN Department d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'cardiology department excelling in pioneering research') AS ref_vec_0\n\nSELECT p.Physician_description, distance(d.Department_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN Department d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'innovative cardiology research department with top specialists') AS ref_vec_0\n\nSELECT p.Physician_description, distance(d.Department_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With aw ON toString(p.EmployeeID) = toString(aw.Physician) JOIN Department d ON toString(aw.Department) = toString(d.DepartmentID) WHERE aw.PrimaryAffiliation = 1\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Both table name and UUID are empty. (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Affiliated_With (\n `Physician` Int64,\n `Department` Int64,\n `PrimaryAffiliation` String\n);\nCREATE TABLE Appointment (\n `AppointmentID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `PrepNurse` Nullable(Int64),\n `Physician` Nullable(Int64),\n `Start` Nullable(String),\n `End` Nullable(String),\n `ExaminationRoom` Nullable(String),\n `Appointment_description` Nullable(String),\n `Appointment_description_embedding` Array(Float32)\n);\nCREATE TABLE Block (\n `BlockFloor` Int64,\n `BlockCode` Int64\n);\nCREATE TABLE Department (\n `DepartmentID` Nullable(Int64),\n `Name` Nullable(String),\n `Head` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Medication (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Brand` Nullable(String),\n `Description` Nullable(String),\n `Medication_description` Nullable(String),\n `Medication_description_embedding` Array(Float32)\n);\nCREATE TABLE Nurse (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `Registered` Nullable(String),\n `SSN` Nullable(Int64),\n `Nurse_description` Nullable(String),\n `Nurse_description_embedding` Array(Float32)\n);\nCREATE TABLE On_Call (\n `Nurse` Int64,\n `BlockFloor` Int64,\n `BlockCode` Int64,\n `OnCallStart` Date,\n `OnCallEnd` Date\n);\nCREATE TABLE Patient (\n `SSN` Nullable(Int64),\n `Name` Nullable(String),\n `Address` Nullable(String),\n `Phone` Nullable(String),\n `InsuranceID` Nullable(Int64),\n `PCP` Nullable(Int64),\n `Patient_description` Nullable(String),\n `Patient_description_embedding` Array(Float32)\n);\nCREATE TABLE Physician (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `SSN` Nullable(Int64),\n `Physician_description` Nullable(String),\n `Physician_description_embedding` Array(Float32)\n);\nCREATE TABLE Prescribes (\n `Physician` Int64,\n `Patient` Int64,\n `Medication` Int64,\n `Date` Date,\n `Appointment` Nullable(Int64),\n `Dose` String\n);\nCREATE TABLE Procedures (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Cost` Nullable(Float64),\n `Procedures_description` Nullable(String),\n `Procedures_description_embedding` Array(Float32)\n);\nCREATE TABLE Room (\n `RoomNumber` Nullable(Int64),\n `RoomType` Nullable(String),\n `BlockFloor` Nullable(Int64),\n `BlockCode` Nullable(Int64),\n `Unavailable` Nullable(String),\n `Room_description` Nullable(String),\n `Room_description_embedding` Array(Float32)\n);\nCREATE TABLE Stay (\n `StayID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `Room` Nullable(Int64),\n `StayStart` Nullable(String),\n `StayEnd` Nullable(String),\n `Stay_description` Nullable(String),\n `Stay_description_embedding` Array(Float32)\n);\nCREATE TABLE Trained_In (\n `Physician` Int64,\n `Treatment` Int64,\n `CertificationDate` Date,\n `CertificationExpires` Date\n);\nCREATE TABLE Undergoes (\n `Patient` Int64,\n `Procedures` Int64,\n `Stay` Int64,\n `DateUndergoes` Date,\n `Physician` Int64,\n `AssistingNurse` Nullable(Int64)\n);" + }, + { + "db_id": "decoration_competition", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned educational institution in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Esteemed professor specializing in quantum physics') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Futuristic technology symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme\nFROM c_filtered AS c\nJOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID)\nJOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID)\nORDER BY c.Name, m.Name, r.Decoration_Theme;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the names of the top 3 colleges renowned as educational institutions in California, the names of the top 3 esteemed professors specializing in quantum physics affiliated with these colleges, and the themes of the top 3 futuristic technology symposiums they may be involved in. Ensure the results are ordered by the college names, member names, and decoration themes.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top educational institutions in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Leading quantum physics professors') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Innovative tech symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prestigious colleges in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Distinguished quantum physics experts') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Advanced technology symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'California''''s top academic institutions') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Quantum physics specialists') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Cutting-edge tech symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading colleges in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Renowned quantum physicists') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Future tech symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Esteemed educational institutions in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top quantum physics lecturers') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Tech innovation symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE college (\n `College_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Leader_Name` Nullable(String),\n `College_Location` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `College_ID` Nullable(Int64),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE round (\n `Round_ID` Nullable(Int64),\n `Member_ID` Nullable(Int64),\n `Decoration_Theme` Nullable(String),\n `Rank_in_Round` Nullable(Int64),\n `Decoration_Theme_embedding` Array(Float32)\n);" + }, + { + "db_id": "book_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A gripping tale of adventure and discovery') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'This book offers a unique insight into the complexities of human nature.') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(Title_embedding, ref_vec_0) AS distance\n FROM book\n WHERE Title_embedding MATCH lembed('all-MiniLM-L6-v2', 'A gripping tale of adventure AND discovery')\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(book_description_embedding, ref_vec_1) AS distance\n FROM book\n\n ORDER BY distance\n LIMIT 5\n),\n\nTitleMatch AS (\n SELECT b.Book_ID, b.Title, b.distance AS title_distance\n FROM b_filtered AS b\n),\n\nDescriptionMatch AS (\n SELECT b.Book_ID, b.book_description, b.distance AS description_distance\n FROM b_filtered AS b\n),\n\nMatchedBooks AS (\n SELECT tm.Book_ID, tm.Title, tm.title_distance, dm.book_description, dm.description_distance\n FROM TitleMatch tm\n JOIN DescriptionMatch dm ON toString(tm.Book_ID) = toString(dm.Book_ID)\n)\n\nSELECT p.Publisher, AVG(p.Price) AS Average_Price\nFROM MatchedBooks mb\nJOIN publication p ON toString(mb.Book_ID) = toString(p.Book_ID)\nGROUP BY p.Publisher\nORDER BY Average_Price DESC;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Determine the average price of books from each publisher, selecting those whose titles are among the top five most relevant to \"A gripping tale of adventure and discovery\" and whose descriptions are among the top five most related to \"This book offers a unique insight into the complexities of human nature.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exhilarating journey of exploration and revelation') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'This book delves into the intricate facets of human psychology.') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(Title_embedding, ref_vec_0) AS distance\n FROM book\n WHERE Title_embedding MATCH lembed('all-MiniLM-L6-v2', 'An exhilarating journey of exploration AND revelation')\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(book_description_embedding, ref_vec_1) AS distance\n FROM book\n\n ORDER BY distance\n LIMIT 5\n),\n\nTitleMatch AS (\n SELECT b.Book_ID, b.Title, b.distance AS title_distance FROM b_filtered AS b\n),\n\nDescriptionMatch AS (\n SELECT b.Book_ID, b.book_description, b.distance AS description_distance FROM b_filtered AS b\n),\n\nMatchedBooks AS (\n SELECT tm.Book_ID, tm.Title, tm.title_distance, dm.book_description, dm.description_distance FROM TitleMatch tm JOIN DescriptionMatch dm ON toString(tm.Book_ID) = toString(dm.Book_ID)\n)\n\nSELECT p.Publisher, AVG(p.Price) AS Average_Price FROM MatchedBooks mb JOIN publication p ON toString(mb.Book_ID) = toString(p.Book_ID) GROUP BY p.Publisher ORDER BY Average_Price DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling story of adventure and discovery') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'This narrative provides a profound understanding of human nature.') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(Title_embedding, ref_vec_0) AS distance\n FROM book\n WHERE Title_embedding MATCH lembed('all-MiniLM-L6-v2', 'A thrilling story of adventure AND discovery')\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(book_description_embedding, ref_vec_1) AS distance\n FROM book\n\n ORDER BY distance\n LIMIT 5\n),\n\nTitleMatch AS (\n SELECT b.Book_ID, b.Title, b.distance AS title_distance FROM b_filtered AS b\n),\n\nDescriptionMatch AS (\n SELECT b.Book_ID, b.book_description, b.distance AS description_distance FROM b_filtered AS b\n),\n\nMatchedBooks AS (\n SELECT tm.Book_ID, tm.Title, tm.title_distance, dm.book_description, dm.description_distance FROM TitleMatch tm JOIN DescriptionMatch dm ON toString(tm.Book_ID) = toString(dm.Book_ID)\n)\n\nSELECT p.Publisher, AVG(p.Price) AS Average_Price FROM MatchedBooks mb JOIN publication p ON toString(mb.Book_ID) = toString(p.Book_ID) GROUP BY p.Publisher ORDER BY Average_Price DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An epic tale of adventure and discovery') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'This book offers a deep dive into the complexities of human behavior.') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(Title_embedding, ref_vec_0) AS distance\n FROM book\n WHERE Title_embedding MATCH lembed('all-MiniLM-L6-v2', 'An epic tale of adventure AND discovery')\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(book_description_embedding, ref_vec_1) AS distance\n FROM book\n\n ORDER BY distance\n LIMIT 5\n),\n\nTitleMatch AS (\n SELECT b.Book_ID, b.Title, b.distance AS title_distance FROM b_filtered AS b\n),\n\nDescriptionMatch AS (\n SELECT b.Book_ID, b.book_description, b.distance AS description_distance FROM b_filtered AS b\n),\n\nMatchedBooks AS (\n SELECT tm.Book_ID, tm.Title, tm.title_distance, dm.book_description, dm.description_distance FROM TitleMatch tm JOIN DescriptionMatch dm ON toString(tm.Book_ID) = toString(dm.Book_ID)\n)\n\nSELECT p.Publisher, AVG(p.Price) AS Average_Price FROM MatchedBooks mb JOIN publication p ON toString(mb.Book_ID) = toString(p.Book_ID) GROUP BY p.Publisher ORDER BY Average_Price DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A captivating saga of adventure and discovery') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'This text explores the nuances of human nature.') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(Title_embedding, ref_vec_0) AS distance\n FROM book\n WHERE Title_embedding MATCH lembed('all-MiniLM-L6-v2', 'A captivating saga of adventure AND discovery')\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(book_description_embedding, ref_vec_1) AS distance\n FROM book\n\n ORDER BY distance\n LIMIT 5\n),\n\nTitleMatch AS (\n SELECT b.Book_ID, b.Title, b.distance AS title_distance FROM b_filtered AS b\n),\n\nDescriptionMatch AS (\n SELECT b.Book_ID, b.book_description, b.distance AS description_distance FROM b_filtered AS b\n),\n\nMatchedBooks AS (\n SELECT tm.Book_ID, tm.Title, tm.title_distance, dm.book_description, dm.description_distance FROM TitleMatch tm JOIN DescriptionMatch dm ON toString(tm.Book_ID) = toString(dm.Book_ID)\n)\n\nSELECT p.Publisher, AVG(p.Price) AS Average_Price FROM MatchedBooks mb JOIN publication p ON toString(mb.Book_ID) = toString(p.Book_ID) GROUP BY p.Publisher ORDER BY Average_Price DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A riveting exploration of adventure and discovery') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'This book provides an insightful look into the intricacies of human character.') AS ref_vec_1,\n\nb_filtered AS (\n SELECT\n *,\n distance(Title_embedding, ref_vec_0) AS distance\n FROM book\n WHERE Title_embedding MATCH lembed('all-MiniLM-L6-v2', 'A riveting exploration of adventure AND discovery')\n ORDER BY distance\n LIMIT 5\n),\n\nb_filtered AS (\n SELECT\n *,\n distance(book_description_embedding, ref_vec_1) AS distance\n FROM book\n\n ORDER BY distance\n LIMIT 5\n),\n\nTitleMatch AS (\n SELECT b.Book_ID, b.Title, b.distance AS title_distance FROM b_filtered AS b\n),\n\nDescriptionMatch AS (\n SELECT b.Book_ID, b.book_description, b.distance AS description_distance FROM b_filtered AS b\n),\n\nMatchedBooks AS (\n SELECT tm.Book_ID, tm.Title, tm.title_distance, dm.book_description, dm.description_distance FROM TitleMatch tm JOIN DescriptionMatch dm ON toString(tm.Book_ID) = toString(dm.Book_ID)\n)\n\nSELECT p.Publisher, AVG(p.Price) AS Average_Price FROM MatchedBooks mb JOIN publication p ON toString(mb.Book_ID) = toString(p.Book_ID) GROUP BY p.Publisher ORDER BY Average_Price DESC;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17033 ('MATCH') (line 10, col 27): MATCH [-0.0012029556091874838, 0.03483918681740761, 0.04952631890773773, 0.03322245180606842, -0.005377664230763912, 0.030328894034028053, 0.09074241667985916, . Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE book (\n `Book_ID` Nullable(Int64),\n `Title` Nullable(String),\n `Issues` Nullable(Float64),\n `Writer` Nullable(String),\n `book_description` Nullable(String),\n `Title_embedding` Array(Float32),\n `book_description_embedding` Array(Float32)\n);\nCREATE TABLE publication (\n `Publication_ID` Nullable(Int64),\n `Book_ID` Nullable(Int64),\n `Publisher` Nullable(String),\n `Publication_Date` Nullable(String),\n `Price` Nullable(Float64)\n);" + }, + { + "db_id": "sports_competition", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A club from the UK established in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance \nFROM club\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the ID of the club that most closely fits the description of being from the UK and established in the late 1990s?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A UK-based club founded in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Club originating from the United Kingdom in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A club established in the UK during the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'British club founded in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Club from the United Kingdom set up in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(String),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE club_rank (\n `Rank` Nullable(Float64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Float64),\n `Silver` Nullable(Float64),\n `Bronze` Nullable(Float64),\n `Total` Nullable(Float64)\n);\nCREATE TABLE competition (\n `Competition_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Competition_type` Nullable(String),\n `Country` Nullable(String),\n `competition_description` Nullable(String),\n `competition_description_embedding` Array(Float32)\n);\nCREATE TABLE competition_result (\n `Competition_ID` Nullable(Int64),\n `Club_ID_1` Nullable(Int64),\n `Club_ID_2` Nullable(Int64),\n `Score` Nullable(String)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `name` Nullable(String),\n `Position` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Apps` Nullable(Float64),\n `Tries` Nullable(Float64),\n `Goals` Nullable(String),\n `Points` Nullable(Float64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "battle_death", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Battle of Pliska ended in a decisive Bulgarian victory against Emperor Nikephoros I in 811.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance\nFROM battle\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a handful of battles that echo the essence of the Battle of Pliska's decisive outcome for the Bulgarians?", + "external_knowledge": "The query utilizes vector search capabilities to perform a semantic comparison between text descriptions. The `MATCH` operator is used to find vectors that are closest in semantic space to the provided embedding, essentially looking for similar descriptions. The `k = 5` indicates that the query is limited to retrieving the top 5 most similar items. In this context, the similarity is not based on exact text matching but on the meaning, as captured by the vector representation using the `all-MiniLM-L6-v2` model. The closer the vectors are in this space, the more semantically similar they are considered.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The Battle of Pliska marked a significant victory for the Bulgarians, defeating Emperor Nikephoros I decisively in 811.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In 811, the Battle of Pliska resulted in a crucial win for Bulgaria, overcoming Emperor Nikephoros I.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The decisive Bulgarian triumph at the Battle of Pliska against Nikephoros I in 811.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Bulgaria''''s pivotal victory at Pliska in 811, defeating Emperor Nikephoros I.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Battle of Pliska in 811 was a defining moment for Bulgaria, with a decisive victory over Emperor Nikephoros I.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE battle (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `date` Nullable(String),\n `bulgarian_commander` Nullable(String),\n `latin_commander` Nullable(String),\n `result` Nullable(String),\n `battle_description` Nullable(String),\n `result_embedding` Array(Float32),\n `battle_description_embedding` Array(Float32)\n);\nCREATE TABLE death (\n `caused_by_ship_id` Nullable(Int64),\n `id` Nullable(Int64),\n `note` Nullable(String),\n `killed` Nullable(Int64),\n `injured` Nullable(Int64),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE ship (\n `lost_in_battle` Nullable(Int64),\n `id` Nullable(Int64),\n `name` Nullable(String),\n `tonnage` Nullable(String),\n `ship_type` Nullable(String),\n `location` Nullable(String),\n `disposition_of_ship` Nullable(String),\n `ship_description` Nullable(String),\n `disposition_of_ship_embedding` Array(Float32),\n `ship_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "employee_hire_evaluation", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A young professional from a major city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance\nFROM employee\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Can you find me the Employee ID of the top young professional who is from a major city? I'm really interested in knowing who fits this description!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A promising young talent based in a metropolitan area') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An emerging professional from a big city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A young expert residing in a major urban center') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A youthful professional located in a large city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top young worker hailing from a significant city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE employee (\n `Employee_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `City` Nullable(String),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE evaluation (\n `Employee_ID` Nullable(String),\n `Year_awarded` Nullable(String),\n `Bonus` Nullable(Float64)\n);\nCREATE TABLE hiring (\n `Shop_ID` Nullable(Int64),\n `Employee_ID` Nullable(Int64),\n `Start_from` Nullable(String),\n `Is_full_time` Nullable(String)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Location` Nullable(String),\n `District` Nullable(String),\n `Number_products` Nullable(Int64),\n `Manager_name` Nullable(String),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "solvency_ii", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A conference discussing advancements in AI technology.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance\nFROM Events e\nJOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few events about AI technology and provide their IDs and details of where they are held?", + "external_knowledge": "In vector search operations, the `MATCH` operator in conjunction with the `lembed` function performs an approximate nearest neighbor (ANN) search. This search is typically used to find items that are semantically similar based on vector embeddings. The `k=3` parameter specifies that the search should return the top 3 most relevant results. The embeddings are compared using the Euclidean distance (L2 norm), where a smaller distance indicates higher similarity. In this context, the query aims to find events that are closely related to the theme of AI technology advancements, and it assumes that \"a few\" refers to the top 3 similar events.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Events focused on AI technology developments.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Meetings discussing the future of AI innovations.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Gatherings about advancements in artificial intelligence.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Seminars on AI technology progress.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Discussions on the latest AI tech trends.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `Address_ID` Int64,\n `address_details` Nullable(String)\n);\nCREATE TABLE Agreements (\n `Document_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets (\n `Asset_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Assets_in_Events (\n `Asset_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Channels (\n `Channel_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Events (\n `Event_ID` Nullable(Int64),\n `Address_ID` Nullable(Int64),\n `Channel_ID` Nullable(Int64),\n `Event_Type_Code` Nullable(String),\n `Finance_ID` Nullable(Int64),\n `Location_ID` Nullable(Int64),\n `Events_description` Nullable(String),\n `Events_description_embedding` Array(Float32)\n);\nCREATE TABLE Events_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Finances (\n `Finance_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Locations (\n `Location_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Locations_description` Nullable(String),\n `Locations_description_embedding` Array(Float32)\n);\nCREATE TABLE Locations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Parties (\n `Party_ID` Nullable(Int64),\n `Party_Details` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties_in_Events (\n `Party_ID` Int64,\n `Event_ID` Int64,\n `Role_Code` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Products (\n `Product_ID` Nullable(Int64),\n `Product_Type_Code` Nullable(String),\n `Product_Name` Nullable(String),\n `Product_Price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_in_Events (\n `Product_in_Event_ID` Int64,\n `Event_ID` Int64,\n `Product_ID` Int64\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "insurance_policies", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Customer search description for analysis') AS ref_vec_0,\n\nRecentClaims AS (\n SELECT \n Claim_ID,\n Policy_ID,\n Amount_Claimed,\n Date_Claim_Made\n FROM \n Claims\n WHERE \n Date_Claim_Made >= '2022-01-01'\n)\n\nSELECT \n c.Customer_ID AS Customer_ID,\n rc.Claim_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance\nFROM \n Customers c\nJOIN \n Customer_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nJOIN \n RecentClaims rc ON toString(cp.Policy_ID) = toString(rc.Policy_ID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you provide me with the IDs of the top 10 customers who have recently made claims and match the description of \"Customer search description for analysis\"? Additionally, what are the claim IDs associated with these customers?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top customers with recent claims for analysis') AS ref_vec_0,\n\nRecentClaims AS (\n SELECT Claim_ID, Policy_ID, Amount_Claimed, Date_Claim_Made FROM Claims WHERE Date_Claim_Made >= '2022-01-01'\n)\n\nSELECT c.Customer_ID, rc.Claim_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customer_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID) JOIN RecentClaims rc ON toString(cp.Policy_ID) = toString(rc.Policy_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of customers with recent claim activity') AS ref_vec_0,\n\nRecentClaims AS (\n SELECT Claim_ID, Policy_ID, Amount_Claimed, Date_Claim_Made FROM Claims WHERE Date_Claim_Made >= '2022-01-01'\n)\n\nSELECT c.Customer_ID, rc.Claim_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customer_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID) JOIN RecentClaims rc ON toString(cp.Policy_ID) = toString(rc.Policy_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigate customers with recent claims') AS ref_vec_0,\n\nRecentClaims AS (\n SELECT Claim_ID, Policy_ID, Amount_Claimed, Date_Claim_Made FROM Claims WHERE Date_Claim_Made >= '2022-01-01'\n)\n\nSELECT c.Customer_ID, rc.Claim_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customer_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID) JOIN RecentClaims rc ON toString(cp.Policy_ID) = toString(rc.Policy_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recent claim activity customer analysis') AS ref_vec_0,\n\nRecentClaims AS (\n SELECT Claim_ID, Policy_ID, Amount_Claimed, Date_Claim_Made FROM Claims WHERE Date_Claim_Made >= '2022-01-01'\n)\n\nSELECT c.Customer_ID, rc.Claim_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customer_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID) JOIN RecentClaims rc ON toString(cp.Policy_ID) = toString(rc.Policy_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Profile of customers with recent claims') AS ref_vec_0,\n\nRecentClaims AS (\n SELECT Claim_ID, Policy_ID, Amount_Claimed, Date_Claim_Made FROM Claims WHERE Date_Claim_Made >= '2022-01-01'\n)\n\nSELECT c.Customer_ID, rc.Claim_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customer_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID) JOIN RecentClaims rc ON toString(cp.Policy_ID) = toString(rc.Policy_ID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'Customers_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Claims (\n `Claim_ID` Nullable(Int64),\n `Policy_ID` Nullable(Int64),\n `Date_Claim_Made` Nullable(String),\n `Date_Claim_Settled` Nullable(String),\n `Amount_Claimed` Nullable(Int64),\n `Amount_Settled` Nullable(Int64),\n `Claims_description` Nullable(String),\n `Claims_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Policies (\n `Policy_ID` Int64,\n `Customer_ID` Int64,\n `Policy_Type_Code` String,\n `Start_Date` Nullable(Date),\n `End_Date` Nullable(Date)\n);\nCREATE TABLE Customers (\n `Customer_ID` Nullable(Int64),\n `Customer_Details` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Payments (\n `Payment_ID` Int64,\n `Settlement_ID` Int64,\n `Payment_Method_Code` Nullable(String),\n `Date_Payment_Made` Nullable(Date),\n `Amount_Payment` Nullable(Int64)\n);\nCREATE TABLE Settlements (\n `Settlement_ID` Nullable(Int64),\n `Claim_ID` Nullable(Int64),\n `Date_Claim_Made` Nullable(String),\n `Date_Claim_Settled` Nullable(String),\n `Amount_Claimed` Nullable(Int64),\n `Amount_Settled` Nullable(Int64),\n `Customer_Policy_ID` Nullable(Int64),\n `Settlements_description` Nullable(String),\n `Settlements_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "device", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Samsung Galaxy on AT&T with Android platform') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT\n Device_ID,\n Device,\n distance(device.device_description_embedding, ref_vec_0) AS distance\n FROM\n device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n s.Shop_Name AS Shop_Name,\n d.Device AS Device\nFROM\n DeviceCTE d\nJOIN\n stock st ON toString(d.Device_ID) = toString(st.Device_ID)\nJOIN\n shop s ON toString(st.Shop_ID) = toString(s.Shop_ID)\nORDER BY\n d.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 8, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the names of shops that have the top 5 devices most similar to a Samsung Galaxy on AT&T with Android platform and display up to 10 such shops ordered by the closeness of the match.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'devices similar to Samsung Galaxy on AT&T with Android OS') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top devices akin to Samsung Galaxy using AT&T and Android') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'similar devices to Samsung Galaxy on AT&T network with Android') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'find devices like Samsung Galaxy with Android on AT&T') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'devices resembling Samsung Galaxy with AT&T and Android') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE device (\n `Device_ID` Nullable(Int64),\n `Device` Nullable(String),\n `Carrier` Nullable(String),\n `Package_Version` Nullable(String),\n `Applications` Nullable(String),\n `Software_Platform` Nullable(String),\n `device_description` Nullable(String),\n `device_description_embedding` Array(Float32)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Shop_Name` Nullable(String),\n `Location` Nullable(String),\n `Open_Date` Nullable(String),\n `Open_Year` Nullable(Int64),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);\nCREATE TABLE stock (\n `Shop_ID` Nullable(Int64),\n `Device_ID` Nullable(Int64),\n `Quantity` Nullable(Int64)\n);" + }, + { + "db_id": "music_4", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An artist known for a groundbreaking album released in the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT \n Artist_ID,\n Artist,\n Famous_Title,\n Famous_Release_date,\n distance(artist.artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.Famous_Title AS Famous_Title\nFROM ArtistKNN a\nJOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID)\nWHERE v.Weeks_on_Top > 2\nORDER BY a.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I am looking for the most notable album title by an artist who had a significant groundbreaking release in the early 2000s. The album should have been on the top charts for more than 2 weeks. Could you provide the title of this album?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An artist who made waves in the early 2000s with a pivotal album release.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Artist with a landmark album from the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Known for a notable album that defined the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An influential artist with a top album from the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Artist famous for a revolutionary early 2000s album.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Artist` Nullable(String),\n `Age` Nullable(Int64),\n `Famous_Title` Nullable(String),\n `Famous_Release_date` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE music_festival (\n `ID` Nullable(Int64),\n `Music_Festival` Nullable(String),\n `Date_of_ceremony` Nullable(String),\n `Category` Nullable(String),\n `Volume` Nullable(Int64),\n `Result` Nullable(String),\n `music_festival_description` Nullable(String),\n `music_festival_description_embedding` Array(Float32)\n);\nCREATE TABLE volume (\n `Volume_ID` Nullable(Int64),\n `Volume_Issue` Nullable(String),\n `Issue_Date` Nullable(String),\n `Weeks_on_Top` Nullable(Float64),\n `Song` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `volume_description` Nullable(String),\n `volume_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "music_4", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A contemporary artist known for their innovative music') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A top-charting song released recently') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT \n a.Artist_ID AS Artist_ID, \n a.Artist AS Artist, \n v.Volume_ID AS Volume_ID, \n v.Song AS Song,\n v.Weeks_on_Top AS Weeks_on_Top,\n v.distance AS vol_distance\n FROM a_filtered AS a\n JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT \n Artist_ID, \n Volume_ID\nFROM\n ArtistVolumeCTE\nORDER BY \n vol_distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 4, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Venture into the musical realm and unearth the IDs of artists and their songs that resonate with the essence of contemporary innovation in music and have recently topped the charts.", + "external_knowledge": "In SQLite with extensions like sqlite-vec and sqlite-lembed, vector searches are employed to find entries based on semantic similarity. The `MATCH` operator performs an approximate nearest neighbor (ANN) search using embeddings generated by models like 'all-MiniLM-L6-v2'. The parameter `k=N` specifies the retrieval of the top N most similar items. Euclidean distance is commonly used to measure similarity, where smaller distances indicate higher similarity. In this context, \"A contemporary artist known for their innovative music\" suggests modern artists pushing musical boundaries, while \"A top-charting song released recently\" signifies songs currently receiving significant attention and acclaim.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An innovative musician shaping modern music trends') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A recent chart-topping hit') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A trailblazing artist in the modern music scene') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A song that recently dominated the charts') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A musician pushing the boundaries of contemporary music') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A song that has recently topped the music charts') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A contemporary artist revolutionizing music innovation') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A recent song that led the charts') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An artist at the forefront of modern musical innovation') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A song that has recently been a chart leader') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Artist` Nullable(String),\n `Age` Nullable(Int64),\n `Famous_Title` Nullable(String),\n `Famous_Release_date` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE music_festival (\n `ID` Nullable(Int64),\n `Music_Festival` Nullable(String),\n `Date_of_ceremony` Nullable(String),\n `Category` Nullable(String),\n `Volume` Nullable(Int64),\n `Result` Nullable(String),\n `music_festival_description` Nullable(String),\n `music_festival_description_embedding` Array(Float32)\n);\nCREATE TABLE volume (\n `Volume_ID` Nullable(Int64),\n `Volume_Issue` Nullable(String),\n `Issue_Date` Nullable(String),\n `Weeks_on_Top` Nullable(Float64),\n `Song` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `volume_description` Nullable(String),\n `volume_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "solvency_ii", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Trade show event featuring innovative technology products') AS ref_vec_0\n\nSELECT p.Product_Name, distance(e.Events_description_embedding, ref_vec_0) AS distance\nFROM Events e\nJOIN Products_in_Events pie ON toString(e.Event_ID) = toString(pie.Event_ID)\nJOIN Products p ON toString(pie.Product_ID) = toString(p.Product_ID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please find the top 5 products featured in a trade show event that highlights innovative technology products? I need to know the names of these products!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Trade exhibition showcasing cutting-edge tech products') AS ref_vec_0\n\nSELECT p.Product_Name, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Products_in_Events pie ON toString(e.Event_ID) = toString(pie.Event_ID) JOIN Products p ON toString(pie.Product_ID) = toString(p.Product_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Expo highlighting innovative technology gadgets') AS ref_vec_0\n\nSELECT p.Product_Name, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Products_in_Events pie ON toString(e.Event_ID) = toString(pie.Event_ID) JOIN Products p ON toString(pie.Product_ID) = toString(p.Product_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Event featuring breakthrough tech products') AS ref_vec_0\n\nSELECT p.Product_Name, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Products_in_Events pie ON toString(e.Event_ID) = toString(pie.Event_ID) JOIN Products p ON toString(pie.Product_ID) = toString(p.Product_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Showcase of innovative technology items') AS ref_vec_0\n\nSELECT p.Product_Name, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Products_in_Events pie ON toString(e.Event_ID) = toString(pie.Event_ID) JOIN Products p ON toString(pie.Product_ID) = toString(p.Product_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Technology fair featuring innovative products') AS ref_vec_0\n\nSELECT p.Product_Name, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Products_in_Events pie ON toString(e.Event_ID) = toString(pie.Event_ID) JOIN Products p ON toString(pie.Product_ID) = toString(p.Product_ID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'Events_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `Address_ID` Int64,\n `address_details` Nullable(String)\n);\nCREATE TABLE Agreements (\n `Document_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets (\n `Asset_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Assets_in_Events (\n `Asset_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Channels (\n `Channel_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Events (\n `Event_ID` Nullable(Int64),\n `Address_ID` Nullable(Int64),\n `Channel_ID` Nullable(Int64),\n `Event_Type_Code` Nullable(String),\n `Finance_ID` Nullable(Int64),\n `Location_ID` Nullable(Int64),\n `Events_description` Nullable(String),\n `Events_description_embedding` Array(Float32)\n);\nCREATE TABLE Events_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Finances (\n `Finance_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Locations (\n `Location_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Locations_description` Nullable(String),\n `Locations_description_embedding` Array(Float32)\n);\nCREATE TABLE Locations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Parties (\n `Party_ID` Nullable(Int64),\n `Party_Details` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties_in_Events (\n `Party_ID` Int64,\n `Event_ID` Int64,\n `Role_Code` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Products (\n `Product_ID` Nullable(Int64),\n `Product_Type_Code` Nullable(String),\n `Product_Name` Nullable(String),\n `Product_Price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_in_Events (\n `Product_in_Event_ID` Int64,\n `Event_ID` Int64,\n `Product_ID` Int64\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "riding_club", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A female athlete from Vancouver with impressive stats and a strong fan base.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance\nFROM player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which player is a female athlete from Vancouver with impressive stats and a strong fan base?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A female sports star from Vancouver known for her remarkable performance and large fan following.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A woman athlete hailing from Vancouver with outstanding stats and a significant number of fans.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent female player from Vancouver with excellent statistics and a devoted fan base.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A female competitor from Vancouver with impressive records and a strong following.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known female athlete from Vancouver with great stats and a loyal fan community.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `Club_name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(Int64),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE coach (\n `Coach_ID` Nullable(Int64),\n `Coach_name` Nullable(String),\n `Gender` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Rank` Nullable(Int64),\n `coach_description` Nullable(String),\n `coach_description_embedding` Array(Float32)\n);\nCREATE TABLE match_result (\n `Rank` Nullable(Int64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Int64),\n `Big_Silver` Nullable(Int64),\n `Small_Silver` Nullable(Int64),\n `Bronze` Nullable(Int64),\n `Points` Nullable(Int64)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Sponsor_name` Nullable(String),\n `Player_name` Nullable(String),\n `Gender` Nullable(String),\n `Residence` Nullable(String),\n `Occupation` Nullable(String),\n `Votes` Nullable(Int64),\n `Rank` Nullable(String),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE player_coach (\n `Player_ID` Nullable(Int64),\n `Coach_ID` Nullable(Int64),\n `Starting_year` Nullable(Int64)\n);" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "SELECT s.first_name, s.last_name, s.email_address\nFROM Degree_Programs dp\nJOIN Student_Enrolment se ON toString(dp.degree_program_id) = toString(se.degree_program_id)\nJOIN Students s ON toString(se.student_id) = toString(s.student_id)\nWHERE dp.degree_summary_description_embedding MATCH lembed(\n 'all-MiniLM-L6-v2',\n 'advanced studies in computer science'\n) AND dp.k = 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 8, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the names and email addresses of students who are enrolled in the top 10 degree programs focused on advanced studies in computer science?", + "external_knowledge": "", + "sql_candidate": [ + "SELECT s.first_name, s.last_name, s.email_address\nFROM Degree_Programs dp\nJOIN Student_Enrolment se ON toString(dp.degree_program_id) = toString(se.degree_program_id)\nJOIN Students s ON toString(se.student_id) = toString(s.student_id)\nWHERE dp.degree_summary_description_embedding MATCH lembed(\n 'all-MiniLM-L6-v2',\n 'advanced studies in computer science'\n) AND dp.k = 10;" + ], + "integration_level": 0, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 282 ('MATCH') (line 5, col 47): MATCH lembed(\n 'all-MiniLM-L6-v2',\n 'advanced studies in computer science'\n) AND dp.k = 10\n FORMAT Native. Expected one of: ParserArrayOfJSONIdentifierDelimiter, token sequence, OpeningSquareBracket, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT, INTO OUTFILE, FORMAT, end of query. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "customers_card_transactions", + "sql": "WITH ActiveCards AS (\n SELECT \n card_id,\n customer_id\n FROM \n Customers_Cards\n WHERE \n date_valid_to > now()\n),\n\n\nRecentTransactions AS (\n SELECT \n ft.transaction_id AS transaction_id,\n ft.account_id AS account_id,\n ft.card_id AS card_id,\n ft.transaction_date AS transaction_date,\n ft.transaction_amount AS transaction_amount,\n ROW_NUMBER() OVER (PARTITION BY ft.card_id ORDER BY ft.transaction_date DESC) AS rn\n FROM \n Financial_Transactions ft\n JOIN \n ActiveCards ac ON toString(ft.card_id) = toString(ac.card_id)\n),\n\n\nCustomerAccounts AS (\n SELECT\n c.customer_id AS customer_id,\n c.customer_first_name || ' ' || c.customer_last_name AS full_name,\n a.account_id AS account_id,\n a.account_name AS account_name\n FROM \n Customers c\n JOIN \n Accounts a ON toString(c.customer_id) = toString(a.customer_id)\n)\n\n\nSELECT \n ca.full_name AS full_name,\n rt.transaction_amount AS transaction_amount\nFROM \n RecentTransactions rt\nJOIN \n CustomerAccounts ca ON toString(rt.account_id) = toString(ca.account_id)\nWHERE \n rt.rn = 1\nORDER BY \n ca.full_name;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Retrieve the full names of customers and the amounts of their most recent transactions from active cards, ordered alphabetically by the customers' full names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH ActiveCards AS (\n SELECT \n card_id,\n customer_id\n FROM \n Customers_Cards\n WHERE \n date_valid_to > now()\n),\n\n\nRecentTransactions AS (\n SELECT \n ft.transaction_id AS transaction_id,\n ft.account_id AS account_id,\n ft.card_id AS card_id,\n ft.transaction_date AS transaction_date,\n ft.transaction_amount AS transaction_amount,\n ROW_NUMBER() OVER (PARTITION BY ft.card_id ORDER BY ft.transaction_date DESC) AS rn\n FROM \n Financial_Transactions ft\n JOIN \n ActiveCards ac ON toString(ft.card_id) = toString(ac.card_id)\n),\n\n\nCustomerAccounts AS (\n SELECT\n c.customer_id AS customer_id,\n c.customer_first_name || ' ' || c.customer_last_name AS full_name,\n a.account_id AS account_id,\n a.account_name AS account_name\n FROM \n Customers c\n JOIN \n Accounts a ON toString(c.customer_id) = toString(a.customer_id)\n)\n\n\nSELECT \n ca.full_name AS full_name,\n rt.transaction_amount AS transaction_amount\nFROM \n RecentTransactions rt\nJOIN \n CustomerAccounts ca ON toString(rt.account_id) = toString(ca.account_id)\nWHERE \n rt.rn = 1\nORDER BY \n ca.full_name;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Accounts (\n `account_id` Nullable(Int64),\n `customer_id` Int64,\n `account_name` Nullable(String),\n `other_account_details` Nullable(String),\n `Accounts_description` Nullable(String)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_first_name` Nullable(String),\n `customer_last_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `other_customer_details` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Customers_Cards (\n `card_id` Nullable(Int64),\n `customer_id` Int64,\n `card_type_code` String,\n `card_number` Nullable(String),\n `date_valid_from` Nullable(Date),\n `date_valid_to` Nullable(Date),\n `other_card_details` Nullable(String)\n);\nCREATE TABLE Financial_Transactions (\n `transaction_id` Int64,\n `previous_transaction_id` Nullable(Int64),\n `account_id` Int64,\n `card_id` Int64,\n `transaction_type` String,\n `transaction_date` Nullable(Date),\n `transaction_amount` Nullable(Float64),\n `transaction_comment` Nullable(String),\n `other_transaction_details` Nullable(String)\n);" + }, + { + "db_id": "game_injury", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling match with a dramatic finish in the final minutes') AS ref_vec_0\n\nSELECT \n g.id AS game_id,\n s.name AS stadium_name,\n i.Player AS injured_player,\n distance(g.game_description_embedding, ref_vec_0) AS similarity_score\nFROM game g\nJOIN stadium s ON toString(g.stadium_id) = toString(s.id)\nLEFT JOIN injury_accident i ON toString(g.id) = toString(i.game_id)\nORDER BY similarity_score\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Which are the top 10 games with an exciting conclusion played in the final moments, including details like the stadium names, injured players, and their similarity scores?", + "external_knowledge": "The `MATCH` operator in the SQL query is used to perform an approximate nearest neighbor (ANN) search, which helps find items that are most similar based on the vectorized form of their descriptions. The `lembed()` function converts text into vectors using a model like 'all-MiniLM-L6-v2', allowing textual similarity to be quantified. The \"k=5\" clause specifies that the search should focus on finding the top 5 closest matches. The similarity is determined using the Euclidean distance (L2 norm) between these vectors, where a smaller distance indicates higher similarity. This technique is commonly used for applications like recommendation systems or content similarity analysis.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exhilarating game with a nail-biting climax in the final moments') AS ref_vec_0\n\nSELECT g.id AS game_id, s.name AS stadium_name, i.Player AS injured_player, distance(g.game_description_embedding, ref_vec_0) AS similarity_score FROM game g JOIN stadium s ON toString(g.stadium_id) = toString(s.id) LEFT JOIN injury_accident i ON toString(g.id) = toString(i.game_id)\nORDER BY similarity_score\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A gripping match with a suspenseful ending in the last minutes') AS ref_vec_0\n\nSELECT g.id AS game_id, s.name AS stadium_name, i.Player AS injured_player, distance(g.game_description_embedding, ref_vec_0) AS similarity_score FROM game g JOIN stadium s ON toString(g.stadium_id) = toString(s.id) LEFT JOIN injury_accident i ON toString(g.id) = toString(i.game_id)\nORDER BY similarity_score\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A captivating game with a thrilling conclusion in the dying moments') AS ref_vec_0\n\nSELECT g.id AS game_id, s.name AS stadium_name, i.Player AS injured_player, distance(g.game_description_embedding, ref_vec_0) AS similarity_score FROM game g JOIN stadium s ON toString(g.stadium_id) = toString(s.id) LEFT JOIN injury_accident i ON toString(g.id) = toString(i.game_id)\nORDER BY similarity_score\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An exciting match with a dramatic finish in the final seconds') AS ref_vec_0\n\nSELECT g.id AS game_id, s.name AS stadium_name, i.Player AS injured_player, distance(g.game_description_embedding, ref_vec_0) AS similarity_score FROM game g JOIN stadium s ON toString(g.stadium_id) = toString(s.id) LEFT JOIN injury_accident i ON toString(g.id) = toString(i.game_id)\nORDER BY similarity_score\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A breathtaking game with an intense ending in the last moments') AS ref_vec_0\n\nSELECT g.id AS game_id, s.name AS stadium_name, i.Player AS injured_player, distance(g.game_description_embedding, ref_vec_0) AS similarity_score FROM game g JOIN stadium s ON toString(g.stadium_id) = toString(s.id) LEFT JOIN injury_accident i ON toString(g.id) = toString(i.game_id)\nORDER BY similarity_score\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'game_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE game (\n `stadium_id` Nullable(Int64),\n `id` Nullable(Int64),\n `Season` Nullable(Int64),\n `Date` Nullable(String),\n `Home_team` Nullable(String),\n `Away_team` Nullable(String),\n `Score` Nullable(String),\n `Competition` Nullable(String),\n `game_description` Nullable(String),\n `game_description_embedding` Array(Float32)\n);\nCREATE TABLE injury_accident (\n `game_id` Nullable(Int64),\n `id` Nullable(Int64),\n `Player` Nullable(String),\n `Injury` Nullable(String),\n `Number_of_matches` Nullable(String),\n `Source` Nullable(String),\n `injury_accident_description` Nullable(String),\n `Injury_embedding` Array(Float32),\n `injury_accident_description_embedding` Array(Float32)\n);\nCREATE TABLE stadium (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Home_Games` Nullable(Int64),\n `Average_Attendance` Nullable(Float64),\n `Total_Attendance` Nullable(Float64),\n `Capacity_Percentage` Nullable(Float64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "phone_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high performance chip model with advanced capabilities') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance \nFROM chip_model\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "** \nCould you help me find one chip model that is known for its high performance and advanced capabilities? I really need its name and the year it was launched! \n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A chip model renowned for its superior performance and cutting-edge features') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A high-performance chip model with state-of-the-art capabilities') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An advanced chip model known for exceptional performance') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-tier chip model with impressive performance and features') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A leading chip model recognized for high performance and advanced technology') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE chip_model (\n `Model_name` Nullable(String),\n `Launch_year` Nullable(Float64),\n `RAM_MiB` Nullable(Float64),\n `ROM_MiB` Nullable(Float64),\n `Slots` Nullable(String),\n `WiFi` Nullable(String),\n `Bluetooth` Nullable(String),\n `chip_model_description` Nullable(String),\n `chip_model_description_embedding` Array(Float32)\n);\nCREATE TABLE chip_model_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE phone (\n `Company_name` Nullable(String),\n `Hardware_Model_name` Nullable(String),\n `Accreditation_type` Nullable(String),\n `Accreditation_level` Nullable(String),\n `Date` Nullable(String),\n `chip_model` Nullable(String),\n `screen_mode` Nullable(String),\n `phone_description` Nullable(String),\n `phone_description_embedding` Array(Float32)\n);\nCREATE TABLE phone_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE screen_mode (\n `Graphics_mode` Nullable(Float64),\n `Char_cells` Nullable(String),\n `Pixels` Nullable(String),\n `Hardware_colours` Nullable(Float64),\n `used_kb` Nullable(Float64),\n `map` Nullable(String),\n `Type` Nullable(String)\n);" + }, + { + "db_id": "election", + "sql": "WITH CountyVectors AS (\n SELECT County_Id, County_name, Population, Zip_code, distance AS county_distance\n FROM county\n WHERE county_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"Baltimore's distinct cultural heritage and growing population\") \n AND k = 3\n),\nPartyVectors AS (\n SELECT Party_ID, Year, Party, Governor, distance AS party_distance\n FROM party\n WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', \"The Democratic party strategies focus on economic growth and social reforms\") \n AND k = 5\n)\nSELECT cv.County_name, pv.Party\nFROM CountyVectors cv\nJOIN PartyVectors pv ON cv.County_Id = pv.Party_ID\nWHERE cv.Population > 50000\nORDER BY cv.County_Id, pv.Year\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the names of 10 counties with a population greater than 50,000 that are most representative of Baltimore's cultural heritage and growing population, and also list the top 5 Democratic parties focusing on economic growth and social reforms associated with these counties?", + "external_knowledge": "", + "sql_candidate": [ + "WITH CountyVectors AS ( SELECT County_Id, County_name, Population, Zip_code, distance AS county_distance FROM county WHERE county_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Counties embodying Baltimore's cultural essence and population growth') AND k = 3 ), PartyVectors AS ( SELECT Party_ID, Year, Party, Governor, distance AS party_distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Democratic initiatives for economic development and social change') AND k = 5 ) SELECT cv.County_name, pv.Party FROM CountyVectors cv JOIN PartyVectors pv ON cv.County_Id = pv.Party_ID WHERE cv.Population > 50000 ORDER BY cv.County_Id, pv.Year LIMIT 10;", + "WITH CountyVectors AS ( SELECT County_Id, County_name, Population, Zip_code, distance AS county_distance FROM county WHERE county_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Baltimore's cultural legacy and demographic expansion') AND k = 3 ), PartyVectors AS ( SELECT Party_ID, Year, Party, Governor, distance AS party_distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Democratic focus on economic progress and societal reforms') AND k = 5 ) SELECT cv.County_name, pv.Party FROM CountyVectors cv JOIN PartyVectors pv ON cv.County_Id = pv.Party_ID WHERE cv.Population > 50000 ORDER BY cv.County_Id, pv.Year LIMIT 10;", + "WITH CountyVectors AS ( SELECT County_Id, County_name, Population, Zip_code, distance AS county_distance FROM county WHERE county_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Counties reflecting Baltimore's cultural heritage and population dynamics') AND k = 3 ), PartyVectors AS ( SELECT Party_ID, Year, Party, Governor, distance AS party_distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Democratic party's economic and social reform agenda') AND k = 5 ) SELECT cv.County_name, pv.Party FROM CountyVectors cv JOIN PartyVectors pv ON cv.County_Id = pv.Party_ID WHERE cv.Population > 50000 ORDER BY cv.County_Id, pv.Year LIMIT 10;", + "WITH CountyVectors AS ( SELECT County_Id, County_name, Population, Zip_code, distance AS county_distance FROM county WHERE county_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Baltimore's cultural identity and population increase') AND k = 3 ), PartyVectors AS ( SELECT Party_ID, Year, Party, Governor, distance AS party_distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Democratic strategies for economic and social advancements') AND k = 5 ) SELECT cv.County_name, pv.Party FROM CountyVectors cv JOIN PartyVectors pv ON cv.County_Id = pv.Party_ID WHERE cv.Population > 50000 ORDER BY cv.County_Id, pv.Year LIMIT 10;", + "WITH CountyVectors AS ( SELECT County_Id, County_name, Population, Zip_code, distance AS county_distance FROM county WHERE county_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Baltimore's cultural and population trends') AND k = 3 ), PartyVectors AS ( SELECT Party_ID, Year, Party, Governor, distance AS party_distance FROM party WHERE party_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Democratic economic growth and social reform policies') AND k = 5 ) SELECT cv.County_name, pv.Party FROM CountyVectors cv JOIN PartyVectors pv ON cv.County_Id = pv.Party_ID WHERE cv.Population > 50000 ORDER BY cv.County_Id, pv.Year LIMIT 10;" + ], + "execution_status": "exception", + "error_message": "约束缺失: 在表 'county' 上的向量搜索缺少 'k=N' 或 'LIMIT N' 约束。", + "db_type": "myscale", + "schema": "CREATE TABLE county (\n `County_Id` Nullable(Int64),\n `County_name` Nullable(String),\n `Population` Nullable(Float64),\n `Zip_code` Nullable(String),\n `county_description` Nullable(String),\n `county_description_embedding` Array(Float32)\n);\nCREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Counties_Represented` Nullable(String),\n `District` Nullable(Int64),\n `Delegate` Nullable(String),\n `Party` Nullable(Int64),\n `First_Elected` Nullable(Float64),\n `Committee` Nullable(String),\n `election_description` Nullable(String),\n `election_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Party` Nullable(String),\n `Governor` Nullable(String),\n `Lieutenant_Governor` Nullable(String),\n `Comptroller` Nullable(String),\n `Attorney_General` Nullable(String),\n `US_Senate` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "sakila_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling adventure of a young hero in a futuristic world') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance\nFROM film\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you find me the film ID for a movie that best represents a thrilling adventure of a young hero in a futuristic world?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exciting journey of a young protagonist in a futuristic setting') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A young hero''''s thrilling quest in a sci-fi world') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A daring adventure of a youthful hero in a future society') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A suspenseful journey of a young hero in a high-tech universe') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling expedition of a young hero in an advanced world') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `actor_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `last_update` Nullable(String),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE address (\n `address_id` Nullable(Int64),\n `address` Nullable(String),\n `address2` Nullable(String),\n `district` Nullable(String),\n `city_id` Nullable(Int64),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `last_update` Nullable(String),\n `address_description` Nullable(String),\n `address_description_embedding` Array(Float32)\n);\nCREATE TABLE category (\n `category_id` Nullable(Int64),\n `name` Nullable(String),\n `last_update` Nullable(String),\n `category_description` Nullable(String),\n `category_description_embedding` Array(Float32)\n);\nCREATE TABLE city (\n `city_id` Nullable(Int64),\n `city` Nullable(String),\n `country_id` Nullable(Int64),\n `last_update` Nullable(String),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE country (\n `country_id` Nullable(Int64),\n `country` Nullable(String),\n `last_update` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `customer_id` Nullable(Int64),\n `store_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `address_id` Nullable(Int64),\n `active` Nullable(String),\n `create_date` Nullable(String),\n `last_update` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE film (\n `film_id` Nullable(Int64),\n `title` Nullable(String),\n `description` Nullable(String),\n `release_year` Nullable(String),\n `language_id` Nullable(Int64),\n `original_language_id` Nullable(Int64),\n `rental_duration` Nullable(Int64),\n `rental_rate` Nullable(Float64),\n `length` Nullable(Int64),\n `replacement_cost` Nullable(Float64),\n `rating` Nullable(String),\n `special_features` Nullable(String),\n `last_update` Nullable(String),\n `title_embedding` Array(Float32),\n `description_embedding` Array(Float32)\n);\nCREATE TABLE film_actor (\n `actor_id` Int64,\n `film_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_category (\n `film_id` Int64,\n `category_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_text (\n `film_id` Int64,\n `title` String,\n `description` Nullable(String)\n);\nCREATE TABLE inventory (\n `inventory_id` Int64,\n `film_id` Int64,\n `store_id` Int64,\n `last_update` String\n);\nCREATE TABLE language (\n `language_id` Int64,\n `name` String,\n `last_update` String\n);\nCREATE TABLE payment (\n `payment_id` Int64,\n `customer_id` Int64,\n `staff_id` Int64,\n `rental_id` Nullable(Int64),\n `amount` Decimal(38, 6),\n `payment_date` Date,\n `last_update` Nullable(String)\n);\nCREATE TABLE rental (\n `rental_id` Int64,\n `rental_date` Date,\n `inventory_id` Int64,\n `customer_id` Int64,\n `return_date` Nullable(Date),\n `staff_id` Int64,\n `last_update` String\n);\nCREATE TABLE staff (\n `staff_id` Int64,\n `first_name` String,\n `last_name` String,\n `address_id` Int64,\n `picture` Nullable(String),\n `email` Nullable(String),\n `store_id` Int64,\n `active` String,\n `username` String,\n `password` Nullable(String),\n `last_update` String,\n `staff_description` Nullable(String)\n);\nCREATE TABLE store (\n `store_id` Int64,\n `manager_staff_id` Int64,\n `address_id` Int64,\n `last_update` String\n);" + }, + { + "db_id": "ship_mission", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A mission description similar to exploring the Arctic waters in the 1960s') AS ref_vec_0\n\nSELECT \n m.Mission_ID AS Mission_ID, \n s.Name AS Ship_Name, \n s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance\nFROM \n mission m\nJOIN \n ship s \nON toString(m.Ship_ID) = toString(s.Ship_ID)\nWHERE \n m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top 5 missions launched after 1950 that are most closely related to the concept of exploring Arctic waters in the 1960s. Please provide the mission IDs, along with the names and types of the ships involved in these missions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'explorations of Arctic waters during the 1960s') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'missions related to Arctic exploration in the 1960s') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '1960s Arctic waters exploration missions') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'investigating Arctic waters in the 1960s') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '1960s missions focused on Arctic sea exploration') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE mission (\n `Mission_ID` Nullable(Int64),\n `Ship_ID` Nullable(Int64),\n `Code` Nullable(String),\n `Launched_Year` Nullable(Int64),\n `Location` Nullable(String),\n `Speed_knots` Nullable(Int64),\n `Fate` Nullable(String),\n `mission_description` Nullable(String),\n `mission_description_embedding` Array(Float32)\n);\nCREATE TABLE mission_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE ship (\n `Ship_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Type` Nullable(String),\n `Nationality` Nullable(String),\n `Tonnage` Nullable(Int64),\n `ship_description` Nullable(String),\n `ship_description_embedding` Array(Float32)\n);\nCREATE TABLE ship_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "customers_campaigns_ecommerce", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Gadgets and devices in Electronics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Customer who buys electronics regularly') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Campaign for electronics from January to June 2023') AS ref_vec_2,\n\nProducts_filtered AS (\n SELECT\n *,\n distance(Products_description_embedding, ref_vec_0) AS distance\n FROM Products\n WHERE Products_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Gadgets AND devices in Electronics')\n ORDER BY distance\n LIMIT 5\n),\n\nCustomers_filtered AS (\n SELECT\n *,\n distance(Customers_description_embedding, ref_vec_1) AS distance\n FROM Customers\n\n ORDER BY distance\n LIMIT 5\n),\n\nMailshot_Campaigns_filtered AS (\n SELECT\n *,\n distance(Mailshot_Campaigns_description_embedding, ref_vec_2) AS distance\n FROM Mailshot_Campaigns\n\n ORDER BY distance\n LIMIT 5\n),\n\nProductMatches AS (\n SELECT product_id, distance\n FROM Products_filtered AS Products\n),\n\nCustomerMatches AS (\n SELECT customer_id, distance\n FROM Customers_filtered AS Customers\n),\n\nMailshotMatches AS (\n SELECT mailshot_id, distance\n FROM Mailshot_Campaigns_filtered AS Mailshot_Campaigns\n)\n\nSELECT mo.order_id\nFROM Customer_Orders mo\nJOIN CustomerMatches cm ON toString(mo.customer_id) = toString(cm.customer_id)\nJOIN Order_Items oi ON toString(mo.order_id) = toString(oi.order_id)\nJOIN ProductMatches pm ON toString(oi.product_id) = toString(pm.product_id)\nJOIN Mailshot_Customers mc ON toString(mc.customer_id) = toString(cm.customer_id)\nJOIN MailshotMatches mm ON toString(mc.mailshot_id) = toString(mm.mailshot_id)\nWHERE mo.order_status_code = 'Delivered'\nORDER BY mo.order_placed_datetime DESC\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the most recent delivered order that includes one of the top 5 products related to gadgets and devices in electronics, purchased by a customer who is among the top 5 individuals known for regularly buying electronics, and who participated in one of the top 5 mailshot campaigns for electronics conducted between January and June 2023.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top electronics gadgets and devices') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Frequent electronics purchaser') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Electronics campaigns Jan-Jun 2023') AS ref_vec_2,\n\nProducts_filtered AS (\n SELECT\n *,\n distance(Products_description_embedding, ref_vec_0) AS distance\n FROM Products\n WHERE Products_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Top electronics gadgets AND devices')\n ORDER BY distance\n LIMIT 5\n),\n\nCustomers_filtered AS (\n SELECT\n *,\n distance(Customers_description_embedding, ref_vec_1) AS distance\n FROM Customers\n\n ORDER BY distance\n LIMIT 5\n),\n\nMailshot_Campaigns_filtered AS (\n SELECT\n *,\n distance(Mailshot_Campaigns_description_embedding, ref_vec_2) AS distance\n FROM Mailshot_Campaigns\n\n ORDER BY distance\n LIMIT 5\n),\n\nProductMatches AS (\n SELECT product_id, distance FROM Products_filtered AS Products\n),\n\nCustomerMatches AS (\n SELECT customer_id, distance FROM Customers_filtered AS Customers\n),\n\nMailshotMatches AS (\n SELECT mailshot_id, distance FROM Mailshot_Campaigns_filtered AS Mailshot_Campaigns\n)\n\nSELECT mo.order_id FROM Customer_Orders mo JOIN CustomerMatches cm ON toString(mo.customer_id) = toString(cm.customer_id) JOIN Order_Items oi ON toString(mo.order_id) = toString(oi.order_id) JOIN ProductMatches pm ON toString(oi.product_id) = toString(pm.product_id) JOIN Mailshot_Customers mc ON toString(mc.customer_id) = toString(cm.customer_id) JOIN MailshotMatches mm ON toString(mc.mailshot_id) = toString(mm.mailshot_id) WHERE mo.order_status_code = 'Delivered' ORDER BY mo.order_placed_datetime DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Popular electronic gadgets and accessories') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Regular electronics consumer') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Top electronics mailshots early 2023') AS ref_vec_2,\n\nProducts_filtered AS (\n SELECT\n *,\n distance(Products_description_embedding, ref_vec_0) AS distance\n FROM Products\n WHERE Products_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Popular electronic gadgets AND accessories')\n ORDER BY distance\n LIMIT 5\n),\n\nCustomers_filtered AS (\n SELECT\n *,\n distance(Customers_description_embedding, ref_vec_1) AS distance\n FROM Customers\n\n ORDER BY distance\n LIMIT 5\n),\n\nMailshot_Campaigns_filtered AS (\n SELECT\n *,\n distance(Mailshot_Campaigns_description_embedding, ref_vec_2) AS distance\n FROM Mailshot_Campaigns\n\n ORDER BY distance\n LIMIT 5\n),\n\nProductMatches AS (\n SELECT product_id, distance FROM Products_filtered AS Products\n),\n\nCustomerMatches AS (\n SELECT customer_id, distance FROM Customers_filtered AS Customers\n),\n\nMailshotMatches AS (\n SELECT mailshot_id, distance FROM Mailshot_Campaigns_filtered AS Mailshot_Campaigns\n)\n\nSELECT mo.order_id FROM Customer_Orders mo JOIN CustomerMatches cm ON toString(mo.customer_id) = toString(cm.customer_id) JOIN Order_Items oi ON toString(mo.order_id) = toString(oi.order_id) JOIN ProductMatches pm ON toString(oi.product_id) = toString(pm.product_id) JOIN Mailshot_Customers mc ON toString(mc.customer_id) = toString(cm.customer_id) JOIN MailshotMatches mm ON toString(mc.mailshot_id) = toString(mm.mailshot_id) WHERE mo.order_status_code = 'Delivered' ORDER BY mo.order_placed_datetime DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading electronics gadgets and tools') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top electronics buyers') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Electronics marketing campaigns Jan to Jun 2023') AS ref_vec_2,\n\nProducts_filtered AS (\n SELECT\n *,\n distance(Products_description_embedding, ref_vec_0) AS distance\n FROM Products\n WHERE Products_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Leading electronics gadgets AND tools')\n ORDER BY distance\n LIMIT 5\n),\n\nCustomers_filtered AS (\n SELECT\n *,\n distance(Customers_description_embedding, ref_vec_1) AS distance\n FROM Customers\n\n ORDER BY distance\n LIMIT 5\n),\n\nMailshot_Campaigns_filtered AS (\n SELECT\n *,\n distance(Mailshot_Campaigns_description_embedding, ref_vec_2) AS distance\n FROM Mailshot_Campaigns\n\n ORDER BY distance\n LIMIT 5\n),\n\nProductMatches AS (\n SELECT product_id, distance FROM Products_filtered AS Products\n),\n\nCustomerMatches AS (\n SELECT customer_id, distance FROM Customers_filtered AS Customers\n),\n\nMailshotMatches AS (\n SELECT mailshot_id, distance FROM Mailshot_Campaigns_filtered AS Mailshot_Campaigns\n)\n\nSELECT mo.order_id FROM Customer_Orders mo JOIN CustomerMatches cm ON toString(mo.customer_id) = toString(cm.customer_id) JOIN Order_Items oi ON toString(mo.order_id) = toString(oi.order_id) JOIN ProductMatches pm ON toString(oi.product_id) = toString(pm.product_id) JOIN Mailshot_Customers mc ON toString(mc.customer_id) = toString(cm.customer_id) JOIN MailshotMatches mm ON toString(mc.mailshot_id) = toString(mm.mailshot_id) WHERE mo.order_status_code = 'Delivered' ORDER BY mo.order_placed_datetime DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Trending gadgets in electronics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Electronics frequent buyers') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Top 2023 electronics campaigns Jan-Jun') AS ref_vec_2,\n\nProducts_filtered AS (\n SELECT\n *,\n distance(Products_description_embedding, ref_vec_0) AS distance\n FROM Products\n\n ORDER BY distance\n LIMIT 5\n),\n\nCustomers_filtered AS (\n SELECT\n *,\n distance(Customers_description_embedding, ref_vec_1) AS distance\n FROM Customers\n\n ORDER BY distance\n LIMIT 5\n),\n\nMailshot_Campaigns_filtered AS (\n SELECT\n *,\n distance(Mailshot_Campaigns_description_embedding, ref_vec_2) AS distance\n FROM Mailshot_Campaigns\n\n ORDER BY distance\n LIMIT 5\n),\n\nProductMatches AS (\n SELECT product_id, distance FROM Products_filtered AS Products\n),\n\nCustomerMatches AS (\n SELECT customer_id, distance FROM Customers_filtered AS Customers\n),\n\nMailshotMatches AS (\n SELECT mailshot_id, distance FROM Mailshot_Campaigns_filtered AS Mailshot_Campaigns\n)\n\nSELECT mo.order_id FROM Customer_Orders mo JOIN CustomerMatches cm ON toString(mo.customer_id) = toString(cm.customer_id) JOIN Order_Items oi ON toString(mo.order_id) = toString(oi.order_id) JOIN ProductMatches pm ON toString(oi.product_id) = toString(pm.product_id) JOIN Mailshot_Customers mc ON toString(mc.customer_id) = toString(cm.customer_id) JOIN MailshotMatches mm ON toString(mc.mailshot_id) = toString(mm.mailshot_id) WHERE mo.order_status_code = 'Delivered' ORDER BY mo.order_placed_datetime DESC LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-demand electronics gadgets') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Regular electronics shoppers') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Electronics mailshots Jan to June 2023') AS ref_vec_2,\n\nProducts_filtered AS (\n SELECT\n *,\n distance(Products_description_embedding, ref_vec_0) AS distance\n FROM Products\n\n ORDER BY distance\n LIMIT 5\n),\n\nCustomers_filtered AS (\n SELECT\n *,\n distance(Customers_description_embedding, ref_vec_1) AS distance\n FROM Customers\n\n ORDER BY distance\n LIMIT 5\n),\n\nMailshot_Campaigns_filtered AS (\n SELECT\n *,\n distance(Mailshot_Campaigns_description_embedding, ref_vec_2) AS distance\n FROM Mailshot_Campaigns\n\n ORDER BY distance\n LIMIT 5\n),\n\nProductMatches AS (\n SELECT product_id, distance FROM Products_filtered AS Products\n),\n\nCustomerMatches AS (\n SELECT customer_id, distance FROM Customers_filtered AS Customers\n),\n\nMailshotMatches AS (\n SELECT mailshot_id, distance FROM Mailshot_Campaigns_filtered AS Mailshot_Campaigns\n)\n\nSELECT mo.order_id FROM Customer_Orders mo JOIN CustomerMatches cm ON toString(mo.customer_id) = toString(cm.customer_id) JOIN Order_Items oi ON toString(mo.order_id) = toString(oi.order_id) JOIN ProductMatches pm ON toString(oi.product_id) = toString(pm.product_id) JOIN Mailshot_Customers mc ON toString(mc.customer_id) = toString(cm.customer_id) JOIN MailshotMatches mm ON toString(mc.mailshot_id) = toString(mm.mailshot_id) WHERE mo.order_status_code = 'Delivered' ORDER BY mo.order_placed_datetime DESC LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 25512 ('MATCH') (line 11, col 42): MATCH [-0.05975080281496048, 0.07261232286691666, -0.004070153459906578, -0.0818004235625267, 0.038650937378406525, -0.054584287106990814, 0.11862944066524506, . Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `premise_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Customer_Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status_code` String,\n `shipping_method_code` String,\n `order_placed_datetime` Date,\n `order_delivered_datetime` Nullable(Date),\n `order_shipping_charges` Nullable(String)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method` Nullable(String),\n `customer_name` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `customer_address` Nullable(String),\n `customer_login` Nullable(String),\n `customer_password` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Mailshot_Campaigns (\n `mailshot_id` Nullable(Int64),\n `product_category` Nullable(String),\n `mailshot_name` Nullable(String),\n `mailshot_start_date` Nullable(String),\n `mailshot_end_date` Nullable(String),\n `Mailshot_Campaigns_description` Nullable(String),\n `Mailshot_Campaigns_description_embedding` Array(Float32)\n);\nCREATE TABLE Mailshot_Customers (\n `mailshot_id` Int64,\n `customer_id` Int64,\n `outcome_code` String,\n `mailshot_customer_date` Nullable(Date)\n);\nCREATE TABLE Order_Items (\n `item_id` Int64,\n `order_item_status_code` String,\n `order_id` Int64,\n `product_id` Int64,\n `item_status_code` Nullable(String),\n `item_delivered_datetime` Nullable(Date),\n `item_order_quantity` Nullable(String)\n);\nCREATE TABLE Premises (\n `premise_id` Nullable(Int64),\n `premises_type` Nullable(String),\n `premise_details` Nullable(String),\n `Premises_description` Nullable(String),\n `premise_details_embedding` Array(Float32)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_category` Nullable(String),\n `product_name` Nullable(String),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "sakila_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Spectacular adventure film starring leading actors') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance\nFROM film f\nJOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id)\nWHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you provide the titles of the top 3 spectacular adventure films in which the actor with ID 5 has starred?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top 3 breathtaking adventure movies featuring actor with ID 5') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading adventure films with actor ID 5 in spectacular roles') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top adventure films starring actor ID 5 in amazing performances') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Spectacular adventure movies with actor ID 5') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best adventure films featuring actor ID 5 in standout roles') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `actor_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `last_update` Nullable(String),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE address (\n `address_id` Nullable(Int64),\n `address` Nullable(String),\n `address2` Nullable(String),\n `district` Nullable(String),\n `city_id` Nullable(Int64),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `last_update` Nullable(String),\n `address_description` Nullable(String),\n `address_description_embedding` Array(Float32)\n);\nCREATE TABLE category (\n `category_id` Nullable(Int64),\n `name` Nullable(String),\n `last_update` Nullable(String),\n `category_description` Nullable(String),\n `category_description_embedding` Array(Float32)\n);\nCREATE TABLE city (\n `city_id` Nullable(Int64),\n `city` Nullable(String),\n `country_id` Nullable(Int64),\n `last_update` Nullable(String),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE country (\n `country_id` Nullable(Int64),\n `country` Nullable(String),\n `last_update` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `customer_id` Nullable(Int64),\n `store_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `address_id` Nullable(Int64),\n `active` Nullable(String),\n `create_date` Nullable(String),\n `last_update` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE film (\n `film_id` Nullable(Int64),\n `title` Nullable(String),\n `description` Nullable(String),\n `release_year` Nullable(String),\n `language_id` Nullable(Int64),\n `original_language_id` Nullable(Int64),\n `rental_duration` Nullable(Int64),\n `rental_rate` Nullable(Float64),\n `length` Nullable(Int64),\n `replacement_cost` Nullable(Float64),\n `rating` Nullable(String),\n `special_features` Nullable(String),\n `last_update` Nullable(String),\n `title_embedding` Array(Float32),\n `description_embedding` Array(Float32)\n);\nCREATE TABLE film_actor (\n `actor_id` Int64,\n `film_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_category (\n `film_id` Int64,\n `category_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_text (\n `film_id` Int64,\n `title` String,\n `description` Nullable(String)\n);\nCREATE TABLE inventory (\n `inventory_id` Int64,\n `film_id` Int64,\n `store_id` Int64,\n `last_update` String\n);\nCREATE TABLE language (\n `language_id` Int64,\n `name` String,\n `last_update` String\n);\nCREATE TABLE payment (\n `payment_id` Int64,\n `customer_id` Int64,\n `staff_id` Int64,\n `rental_id` Nullable(Int64),\n `amount` Decimal(38, 6),\n `payment_date` Date,\n `last_update` Nullable(String)\n);\nCREATE TABLE rental (\n `rental_id` Int64,\n `rental_date` Date,\n `inventory_id` Int64,\n `customer_id` Int64,\n `return_date` Nullable(Date),\n `staff_id` Int64,\n `last_update` String\n);\nCREATE TABLE staff (\n `staff_id` Int64,\n `first_name` String,\n `last_name` String,\n `address_id` Int64,\n `picture` Nullable(String),\n `email` Nullable(String),\n `store_id` Int64,\n `active` String,\n `username` String,\n `password` Nullable(String),\n `last_update` String,\n `staff_description` Nullable(String)\n);\nCREATE TABLE store (\n `store_id` Int64,\n `manager_staff_id` Int64,\n `address_id` Int64,\n `last_update` String\n);" + }, + { + "db_id": "riding_club", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent player sponsored by a leading sponsor') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A renowned club based in the USA') AS ref_vec_1,\n\nplayer_filtered AS (\n SELECT\n *,\n distance(player_description_embedding, ref_vec_0) AS distance\n FROM player\n\n ORDER BY distance\n LIMIT 5\n),\n\nclub_filtered AS (\n SELECT\n *,\n distance(club_description_embedding, ref_vec_1) AS distance\n FROM club\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_players AS (\n SELECT Player_ID, distance \n FROM player_filtered AS player\n),\n\nplayer_coach_info AS (\n SELECT sp.Player_ID, pc.Coach_ID, c.Coach_name, c.Rank \n FROM similar_players sp\n JOIN player_coach pc ON toString(sp.Player_ID) = toString(pc.Player_ID)\n JOIN coach c ON toString(pc.Coach_ID) = toString(c.Coach_ID)\n),\n\nsimilar_clubs AS (\n SELECT Club_ID, distance \n FROM club_filtered AS club\n)\n\nSELECT pi.Player_name, COUNT(mr.Gold) AS Gold_medals\nFROM player_coach_info pci\nJOIN player pi ON toString(pci.Player_ID) = toString(pi.Player_ID)\nJOIN similar_clubs sc ON toString(pci.Coach_ID) = toString(sc.Club_ID)\nJOIN match_result mr ON toString(sc.Club_ID) = toString(mr.Club_ID)\nGROUP BY pi.Player_name\nORDER BY Gold_medals DESC\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the names of the top 5 players who have won the most gold medals and are associated with top clubs and coaches? The players should be prominent ones sponsored by leading sponsors, and the clubs should be renowned ones based in the USA.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An elite athlete backed by a major sponsor') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A prestigious club located in the USA') AS ref_vec_1,\n\nplayer_filtered AS (\n SELECT\n *,\n distance(player_description_embedding, ref_vec_0) AS distance\n FROM player\n\n ORDER BY distance\n LIMIT 5\n),\n\nclub_filtered AS (\n SELECT\n *,\n distance(club_description_embedding, ref_vec_1) AS distance\n FROM club\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_players AS (\n SELECT Player_ID, distance FROM player_filtered AS player\n),\n\nplayer_coach_info AS (\n SELECT sp.Player_ID, pc.Coach_ID, c.Coach_name, c.Rank FROM similar_players sp JOIN player_coach pc ON toString(sp.Player_ID) = toString(pc.Player_ID) JOIN coach c ON toString(pc.Coach_ID) = toString(c.Coach_ID)\n),\n\nsimilar_clubs AS (\n SELECT Club_ID, distance FROM club_filtered AS club\n)\n\nSELECT pi.Player_name, COUNT(mr.Gold) AS Gold_medals FROM player_coach_info pci JOIN player pi ON toString(pci.Player_ID) = toString(pi.Player_ID) JOIN similar_clubs sc ON toString(pci.Coach_ID) = toString(sc.Club_ID) JOIN match_result mr ON toString(sc.Club_ID) = toString(mr.Club_ID) GROUP BY pi.Player_name ORDER BY Gold_medals DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-tier player with high-profile sponsorship') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A well-known club situated in the USA') AS ref_vec_1,\n\nplayer_filtered AS (\n SELECT\n *,\n distance(player_description_embedding, ref_vec_0) AS distance\n FROM player\n\n ORDER BY distance\n LIMIT 5\n),\n\nclub_filtered AS (\n SELECT\n *,\n distance(club_description_embedding, ref_vec_1) AS distance\n FROM club\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_players AS (\n SELECT Player_ID, distance FROM player_filtered AS player\n),\n\nplayer_coach_info AS (\n SELECT sp.Player_ID, pc.Coach_ID, c.Coach_name, c.Rank FROM similar_players sp JOIN player_coach pc ON toString(sp.Player_ID) = toString(pc.Player_ID) JOIN coach c ON toString(pc.Coach_ID) = toString(c.Coach_ID)\n),\n\nsimilar_clubs AS (\n SELECT Club_ID, distance FROM club_filtered AS club\n)\n\nSELECT pi.Player_name, COUNT(mr.Gold) AS Gold_medals FROM player_coach_info pci JOIN player pi ON toString(pci.Player_ID) = toString(pi.Player_ID) JOIN similar_clubs sc ON toString(pci.Coach_ID) = toString(sc.Club_ID) JOIN match_result mr ON toString(sc.Club_ID) = toString(mr.Club_ID) GROUP BY pi.Player_name ORDER BY Gold_medals DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A distinguished player with major sponsorship') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A famous club based in the USA') AS ref_vec_1,\n\nplayer_filtered AS (\n SELECT\n *,\n distance(player_description_embedding, ref_vec_0) AS distance\n FROM player\n\n ORDER BY distance\n LIMIT 5\n),\n\nclub_filtered AS (\n SELECT\n *,\n distance(club_description_embedding, ref_vec_1) AS distance\n FROM club\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_players AS (\n SELECT Player_ID, distance FROM player_filtered AS player\n),\n\nplayer_coach_info AS (\n SELECT sp.Player_ID, pc.Coach_ID, c.Coach_name, c.Rank FROM similar_players sp JOIN player_coach pc ON toString(sp.Player_ID) = toString(pc.Player_ID) JOIN coach c ON toString(pc.Coach_ID) = toString(c.Coach_ID)\n),\n\nsimilar_clubs AS (\n SELECT Club_ID, distance FROM club_filtered AS club\n)\n\nSELECT pi.Player_name, COUNT(mr.Gold) AS Gold_medals FROM player_coach_info pci JOIN player pi ON toString(pci.Player_ID) = toString(pi.Player_ID) JOIN similar_clubs sc ON toString(pci.Coach_ID) = toString(sc.Club_ID) JOIN match_result mr ON toString(sc.Club_ID) = toString(mr.Club_ID) GROUP BY pi.Player_name ORDER BY Gold_medals DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A celebrated athlete with leading sponsorship') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A reputable club in the USA') AS ref_vec_1,\n\nplayer_filtered AS (\n SELECT\n *,\n distance(player_description_embedding, ref_vec_0) AS distance\n FROM player\n\n ORDER BY distance\n LIMIT 5\n),\n\nclub_filtered AS (\n SELECT\n *,\n distance(club_description_embedding, ref_vec_1) AS distance\n FROM club\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_players AS (\n SELECT Player_ID, distance FROM player_filtered AS player\n),\n\nplayer_coach_info AS (\n SELECT sp.Player_ID, pc.Coach_ID, c.Coach_name, c.Rank FROM similar_players sp JOIN player_coach pc ON toString(sp.Player_ID) = toString(pc.Player_ID) JOIN coach c ON toString(pc.Coach_ID) = toString(c.Coach_ID)\n),\n\nsimilar_clubs AS (\n SELECT Club_ID, distance FROM club_filtered AS club\n)\n\nSELECT pi.Player_name, COUNT(mr.Gold) AS Gold_medals FROM player_coach_info pci JOIN player pi ON toString(pci.Player_ID) = toString(pi.Player_ID) JOIN similar_clubs sc ON toString(pci.Coach_ID) = toString(sc.Club_ID) JOIN match_result mr ON toString(sc.Club_ID) = toString(mr.Club_ID) GROUP BY pi.Player_name ORDER BY Gold_medals DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A leading player with top sponsorships') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A renowned club in the United States') AS ref_vec_1,\n\nplayer_filtered AS (\n SELECT\n *,\n distance(player_description_embedding, ref_vec_0) AS distance\n FROM player\n\n ORDER BY distance\n LIMIT 5\n),\n\nclub_filtered AS (\n SELECT\n *,\n distance(club_description_embedding, ref_vec_1) AS distance\n FROM club\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_players AS (\n SELECT Player_ID, distance FROM player_filtered AS player\n),\n\nplayer_coach_info AS (\n SELECT sp.Player_ID, pc.Coach_ID, c.Coach_name, c.Rank FROM similar_players sp JOIN player_coach pc ON toString(sp.Player_ID) = toString(pc.Player_ID) JOIN coach c ON toString(pc.Coach_ID) = toString(c.Coach_ID)\n),\n\nsimilar_clubs AS (\n SELECT Club_ID, distance FROM club_filtered AS club\n)\n\nSELECT pi.Player_name, COUNT(mr.Gold) AS Gold_medals FROM player_coach_info pci JOIN player pi ON toString(pci.Player_ID) = toString(pi.Player_ID) JOIN similar_clubs sc ON toString(pci.Coach_ID) = toString(sc.Club_ID) JOIN match_result mr ON toString(sc.Club_ID) = toString(mr.Club_ID) GROUP BY pi.Player_name ORDER BY Gold_medals DESC LIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There's no column 'pci.Player_ID' in table 'pci': While processing WITH [0.03809935599565506, 0.07690577954053879, 0.007264675106853247, -0.1151064783334732, 0.07189846783876419, 0.0819230005145073, 0.0876774713397026, 0.09414847940206528, 0.025295568630099297, 0.03221889212727547, -0.039836086332798004, -0.0007991374004632235, -0.03239870071411133, 0.04316283389925957, 0.007589349523186684, -0.01989843137562275, 0.005645907483994961, -0.036671292036771774, -0.014303561300039291, -0.04506329819560051, -0.052926816046237946, -0.08781615644693375, 0.000040648432332091033, 0.015284864231944084, -0.053915299475193024, -0.02565545029938221, 0.05039346590638161, 0.028580471873283386, 0.020425381138920784, -0.018716419115662575, 0.002138599054887891, -0.05239862948656082, 0.04316836968064308, 0.038681115955114365, -0.03255046531558037, 0.03302382305264473, -0.020967544987797737, 0.01326723676174879, -0.07404326647520065, 0.04344874247908592, 0.06837015599012375, 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-0.047496262937784195, -0.006871058605611324, 0.004832559265196323, 0.07948669046163559, 0.028186175972223282, -0.04370598867535591, 0.03475518152117729, 0.04796069860458374, -1.7059830881294147e-8, -0.08960594236850739, -0.0034752299543470144, -0.07602144032716751, -0.01634431816637516, 0.0026026905979961157, -0.017593586817383766, -0.06601225584745407, -0.12665261328220367, 0.04188527166843414, 0.0007672743522562087, 0.007685392163693905, -0.012926295399665833, 0.04586072266101837, -0.0027289805002510548, 0.018945589661598206, -0.04698782041668892, -0.08574413508176804, 0.07304586470127106, -0.07711677253246307, 0.03313102573156357, 0.004629469010978937, 0.028791969642043114, 0.035082168877124786, -0.00088706478709355, 0.005696375388652086, -0.020502490922808647, -0.07460161298513412, 0.048386700451374054, 0.010107213631272316, -0.048185575753450394, -0.008063353598117828, 0.040202248841524124, -0.005194291938096285, -0.06136408448219299, 0.09526330232620239, 0.030199311673641205, -0.0009749960736371577, -0.06452306360006332, -0.01115900743752718, 0.036857493221759796, 0.006020554341375828, 0.009001847356557846, -0.04623190686106682, 0.07774197310209274, -0.008702504448592663, 0.06988516449928284, -0.10051682591438293, -0.022666392847895622, -0.07508284598588943, 0.0018610882107168436, -0.02816610597074032, 0.0076903109438717365, 0.017891820520162582, -0.020343607291579247, -0.05059297755360603, -0.016333509236574173, -0.027055082842707634, 0.09426979720592499, -0.0042614140547811985, -0.06336235255002975, -0.02048756554722786, -0.09782920032739639, 0.06863006949424744, 0.04749844968318939] AS ref_vec_0, [0.05102061107754707, -0.0849027931690216, -0.09318007528781891, 0.08463717997074127, -0.0515214167535305, 0.030091824010014534, -0.021013351157307625, -0.0389556959271431, -0.021724188700318336, 0.010306201875209808, 0.02805366739630699, 0.0214694831520319, 0.011420550756156445, 0.03585970401763916, -0.04707350581884384, 0.0033347993157804012, 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-0.011115586385130882, 0.01309985015541315, 0.014616910368204117, 0.03841773793101311, 0.02706761658191681, 0.04559817165136337, -0.01456634234637022, 0.05499405041337013, 0.017480529844760895, 0.059754569083452225, -0.07494446635246277, 0.03162865340709686, 0.05122559145092964, -0.08083295077085495, -0.03569316864013672, 0.008666194044053555, 0.008220070973038673, 0.04815549775958061, -0.03371293470263481, -0.036678340286016464, 0.035181816667318344, -0.09949000924825668, 0.03499656543135643, -0.03235369548201561] AS ref_vec_1 SELECT *, distance(club_description_embedding, ref_vec_1) AS distance FROM club ORDER BY distance ASC LIMIT 5), similar_players AS (WITH [0.03809935599565506, 0.07690577954053879, 0.007264675106853247, -0.1151064783334732, 0.07189846783876419, 0.0819230005145073, 0.0876774713397026, 0.09414847940206528, 0.025295568630099297, 0.03221889212727547, -0.039836086332798004, -0.0007991374004632235, -0.03239870071411133, 0.04316283389925957, 0.007589349523186684, 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toString(sp.Player_ID) = toString(pc.Player_ID) INNER JOIN coach AS c ON toString(pc.Coach_ID) = toString(c.Coach_ID)), similar_clubs AS (WITH [0.03809935599565506, 0.07690577954053879, 0.007264675106853247, -0.1151064783334732, 0.07189846783876419, 0.0819230005145073, 0.0876774713397026, 0.09414847940206528, 0.025295568630099297, 0.03221889212727547, -0.039836086332798004, -0.0007991374004632235, -0.03239870071411133, 0.04316283389925957, 0.007589349523186684, -0.01989843137562275, 0.005645907483994961, -0.036671292036771774, -0.014303561300039291, -0.04506329819560051, -0.052926816046237946, -0.08781615644693375, 0.000040648432332091033, 0.015284864231944084, -0.053915299475193024, -0.02565545029938221, 0.05039346590638161, 0.028580471873283386, 0.020425381138920784, -0.018716419115662575, 0.002138599054887891, -0.05239862948656082, 0.04316836968064308, 0.038681115955114365, -0.03255046531558037, 0.03302382305264473, -0.020967544987797737, 0.01326723676174879, -0.07404326647520065, 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-0.006369273643940687, 0.04367048665881157, -0.08440033346414566, 0.12461494654417038, 0.027968794107437134, -0.057449351996183395, -0.050466883927583694, 0.05383194983005524, -0.0838414877653122, -0.011691030114889145, -0.031646691262722015, -0.04685976728796959, -0.0022579319775104523, 0.07303155213594437, -0.08969259262084961, -0.01920161210000515, 0.031908176839351654, 0.0379362553358078, 0.00010989117436110973, 0.017659928649663925, 0.008383694104850292, -0.03681183606386185, -0.04117302969098091, 0.09237292408943176, 0.03729863092303276, 0.06503485143184662, 0.018237370997667313, 0.043322183191776276, 0.029122833162546158, 0.09198514372110367, 0.050981443375349045, -0.01826486922800541, -0.055143747478723526, -0.05984082818031311, -0.025402596220374107, -1.7405129781877804e-8, -0.04127204790711403, 0.013209855183959007, -0.024947255849838257, 0.08655758947134018, -0.02305607870221138, -0.018743369728326797, 0.006498604081571102, -0.02950848639011383, -0.01835932396352291, 0.07163836807012558, -0.07508967071771622, -0.03437889739871025, 0.012481141835451126, 0.008941336534917355, -0.036291491240262985, -0.056636564433574677, -0.028459912165999413, 0.10943485051393509, -0.022840457037091255, 0.06067900359630585, -0.006321018096059561, 0.0060533760115504265, -0.0014053726335987449, -0.02349991723895073, -0.015719976276159286, -0.0399269200861454, -0.04802723228931427, -0.053233109414577484, -0.05805898457765579, -0.06892146170139313, -0.024700211361050606, 0.06779850274324417, 0.01921008713543415, -0.024290526285767555, -0.017045672982931137, -0.004070872440934181, -0.03866037726402283, -0.045785319060087204, -0.05596661940217018, -0.044148605316877365, -0.011115586385130882, 0.01309985015541315, 0.014616910368204117, 0.03841773793101311, 0.02706761658191681, 0.04559817165136337, -0.01456634234637022, 0.05499405041337013, 0.017480529844760895, 0.059754569083452225, -0.07494446635246277, 0.03162865340709686, 0.05122559145092964, -0.08083295077085495, -0.03569316864013672, 0.008666194044053555, 0.008220070973038673, 0.04815549775958061, -0.03371293470263481, -0.036678340286016464, 0.035181816667318344, -0.09949000924825668, 0.03499656543135643, -0.03235369548201561] AS ref_vec_1 SELECT Club_ID, distance FROM club_filtered AS club) SELECT Player_name AS `pi.Player_name`, COUNT(Gold) AS Gold_medals FROM player_coach_info AS pci INNER JOIN player AS pi ON toString(pci.Player_ID) = toString(pi.Player_ID) INNER JOIN similar_clubs AS sc ON toString(pci.Coach_ID) = toString(sc.Club_ID) INNER JOIN match_result AS mr ON toString(sc.Club_ID) = toString(mr.Club_ID) GROUP BY Player_name ORDER BY Gold_medals DESC LIMIT 5. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `Club_name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(Int64),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE coach (\n `Coach_ID` Nullable(Int64),\n `Coach_name` Nullable(String),\n `Gender` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Rank` Nullable(Int64),\n `coach_description` Nullable(String),\n `coach_description_embedding` Array(Float32)\n);\nCREATE TABLE match_result (\n `Rank` Nullable(Int64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Int64),\n `Big_Silver` Nullable(Int64),\n `Small_Silver` Nullable(Int64),\n `Bronze` Nullable(Int64),\n `Points` Nullable(Int64)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Sponsor_name` Nullable(String),\n `Player_name` Nullable(String),\n `Gender` Nullable(String),\n `Residence` Nullable(String),\n `Occupation` Nullable(String),\n `Votes` Nullable(Int64),\n `Rank` Nullable(String),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE player_coach (\n `Player_ID` Nullable(Int64),\n `Coach_ID` Nullable(Int64),\n `Starting_year` Nullable(Int64)\n);" + }, + { + "db_id": "yelp", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An amazing dining experience with exquisite dishes.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT \n r.rid AS rid, \n r.business_id AS business_id, \n r.user_id AS user_id, \n r.text AS text, \n r.rating AS review_rating, \n distance(r.text_embedding, ref_vec_0) AS distance\n FROM \n review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n b.name AS name\nFROM\n CTE_Reviews cr\nJOIN\n business b ON toString(cr.business_id) = toString(b.business_id)\nWHERE\n b.city = 'San Francisco'\n AND b.rating > 4.0\nORDER BY\n cr.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "What are the names of the top five culinary havens in San Francisco, where diners have sung praises of mouth-watering delicacies, and whose glory shines with ratings above 4.0?", + "external_knowledge": "The vector search operation using `MATCH` performs an approximate nearest neighbor search, which tries to find the closest matches in a vector space based on a specified query embedding. Here, `lembed('all-MiniLM-L6-v2', \"An amazing dining experience with exquisite dishes.\")` generates an embedding for the phrase that is compared against the embeddings of review texts. The parameter `k = 5` specifies the retrieval of the top five reviews that are most similar to the vector query, sorted by Euclidean distance (L2 norm), where smaller distances indicate higher similarity. This technique allows semantic matching beyond simple text comparison, ideal for uncovering nuanced similarities in textual descriptions.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A delightful culinary journey with mouth-watering flavors.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional dining with highly praised dishes.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-rated restaurants with delicious meals.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly acclaimed eateries with outstanding cuisine.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned dining spots with rave reviews.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE business (\n `bid` Nullable(Int64),\n `business_id` Nullable(String),\n `name` Nullable(String),\n `full_address` Nullable(String),\n `city` Nullable(String),\n `latitude` Nullable(String),\n `longitude` Nullable(String),\n `review_count` Nullable(Int64),\n `is_open` Nullable(Int64),\n `rating` Nullable(Float64),\n `state` Nullable(String),\n `business_description` Nullable(String)\n);\nCREATE TABLE category (\n `id` Nullable(Int64),\n `business_id` Nullable(String),\n `category_name` Nullable(String),\n `category_description` Nullable(String)\n);\nCREATE TABLE checkin (\n `cid` Nullable(Int64),\n `business_id` Nullable(String),\n `count` Nullable(Int64),\n `day` Nullable(String)\n);\nCREATE TABLE neighbourhood (\n `id` Nullable(Int64),\n `business_id` Nullable(String),\n `neighbourhood_name` Nullable(String),\n `neighbourhood_description` Nullable(String)\n);\nCREATE TABLE review (\n `rid` Nullable(Int64),\n `business_id` Nullable(String),\n `user_id` Nullable(String),\n `rating` Nullable(Float64),\n `text` Nullable(String),\n `year` Nullable(Int64),\n `month` Nullable(String),\n `text_embedding` Array(Float32)\n);\nCREATE TABLE review_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE tip (\n `tip_id` Nullable(Int64),\n `business_id` Nullable(String),\n `text` Nullable(String),\n `user_id` Nullable(String),\n `likes` Nullable(Int64),\n `year` Nullable(Int64),\n `month` Nullable(String),\n `text_embedding` Array(Float32)\n);\nCREATE TABLE tip_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE user (\n `uid` Nullable(Int64),\n `user_id` Nullable(String),\n `name` Nullable(String),\n `user_description` Nullable(String)\n);" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Introduction to data science techniques and applications') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance\nFROM Courses\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the course that best aligns with the description \"Introduction to data science techniques and applications\" and provide its name.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Basics of data science methods and their applications') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Introductory course on data science principles and practices') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Fundamentals of data science and its practical uses') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Overview of data science approaches and real-world applications') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Introduction to data science concepts and their implementation') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "ship_mission", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A mission launched in the early 20th century capable of high speed') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance\nFROM mission\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Which mission launched in the early 20th century, known for high speed, closely fits that description?", + "external_knowledge": "- The `MATCH` operator is used in vector similarity searches to find items that are close in semantic space to a given input.\n- The `lembed()` function converts text into a vector representation using a specific machine learning model, in this case, `all-MiniLM-L6-v2`.\n- The parameter `k = 1` indicates that only one result, the most similar mission, is to be returned.\n- The similarity is measured using Euclidean distance (L2 norm), where a smaller distance signifies a higher similarity.\n- External knowledge: The phrase \"early 20th century\" refers to the period from 1900 to 1930, and \"high speed\" suggests missions with above-average velocity for that era.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A fast mission from the early 20th century') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-speed mission launched in the early 1900s') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Early 20th-century mission known for speed') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A mission from the early 1900s with high velocity') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rapid mission initiated in the early 20th century') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE mission (\n `Mission_ID` Nullable(Int64),\n `Ship_ID` Nullable(Int64),\n `Code` Nullable(String),\n `Launched_Year` Nullable(Int64),\n `Location` Nullable(String),\n `Speed_knots` Nullable(Int64),\n `Fate` Nullable(String),\n `mission_description` Nullable(String),\n `mission_description_embedding` Array(Float32)\n);\nCREATE TABLE mission_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE ship (\n `Ship_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Type` Nullable(String),\n `Nationality` Nullable(String),\n `Tonnage` Nullable(Int64),\n `ship_description` Nullable(String),\n `ship_description_embedding` Array(Float32)\n);\nCREATE TABLE ship_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "flight_company", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Airport in Amsterdam, Netherlands') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Company incorporated in China') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description\nFROM flight f\nJOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id)\nJOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id)\nORDER BY f.id\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find a flight that involves a major airport in Amsterdam and is operated by a prominent company from China?", + "external_knowledge": "The vector search operations use the MATCH operator from the `sqlite-lembed` extension to perform approximate nearest neighbor (ANN) searches. The `lembed` function is applied to generate embeddings based on the provided descriptions. The parameter `k=5` specifies that the query retrieves the top 5 closest entities (for both airports and companies) as determined by their vector embedding similarity, typically using the Euclidean distance (L2 norm). In this context, a \"major airport\" or \"prominent company\" refers to those entities that are closest in description to the specified criteria, namely airports in Amsterdam and companies incorporated in China.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Major airport in Amsterdam') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Leading Chinese airline') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Amsterdam international airport') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top airline from China') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Amsterdam Schiphol Airport') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Prominent Chinese aviation company') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Airport located in Amsterdam') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Chinese airline operator') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hub airport in Amsterdam') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major airline from China') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE airport (\n `id` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `IATA` Nullable(String),\n `ICAO` Nullable(String),\n `name` Nullable(String),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `id` Nullable(Int64),\n `Vehicle_Flight_number` Nullable(String),\n `Date` Nullable(String),\n `Pilot` Nullable(String),\n `Velocity` Nullable(Float64),\n `Altitude` Nullable(Float64),\n `airport_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);\nCREATE TABLE operate_company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Type` Nullable(String),\n `Principal_activities` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `operate_company_description` Nullable(String),\n `operate_company_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "flight_company", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Reykjavik Airport (IATA: RKV, ICAO: BIRK) is located in Reykjavik, Iceland.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Icelandair is a Corporate entity operating as an Airline, incorporated in Iceland with a Group Equity Shareholding of 25.00%') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Flight TF-101 was piloted by Smith on July 15, 2023, at a velocity of 560.0 mph and an altitude of 39000.0 feet, from airport 10 under company 20.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n),\n\nf_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_2) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n f.Vehicle_Flight_number AS Vehicle_Flight_number, \n f.Date AS Date\nFROM f_filtered AS f\nJOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id)\nJOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id)\n WHERE f.flight_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Flight TF-101 was piloted by Smith on July 15, 2023, at a velocity of 560.0 mph AND an altitude of 39000.0 feet, from airport 10 under company 20.') ORDER BY \n f.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "** \nIn the realm of soaring ambitions, could you uncover the top ten journeys that departed from Reykjavik and were guided under the Icelandair banner? The skies remember the flight TF-101 piloted by Smith on a summer day. \n\n**", + "external_knowledge": "** \nThis query uses vector embeddings to perform approximate nearest neighbor searches, which identify entries most similar to a given text. The `MATCH` operator in SQLite with `lembed()` helps compare embeddings using the Euclidean distance (L2 norm), retrieving items with the smallest distance as most similar. The `k=5` clause specifies the top five closest items to each condition, capturing the essence of locations, companies, and flights. The embedding comparison allows for nuanced retrieval akin to finding thematic or semantic closeness, focusing on the details like specific airports and airlines. \n\n**", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Reykjavik Airport serves as a key hub in Iceland, recognized by its IATA code RKV.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Icelandair is known for its extensive flight operations from Reykjavik, Iceland.') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Flight TF-101, commanded by Smith, departed on a summer day, reaching high altitudes.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n),\n\nf_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_2) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.Vehicle_Flight_number, f.Date FROM f_filtered AS f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'RKV, situated in Reykjavik, is a prominent airport in Iceland.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Icelandair, with its headquarters in Iceland, conducts flights from Reykjavik.') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'On a warm summer day, Smith piloted Flight TF-101 from Reykjavik.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n),\n\nf_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_2) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.Vehicle_Flight_number, f.Date FROM f_filtered AS f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Located in Reykjavik, RKV airport is a vital aviation hub for Iceland.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Icelandair operates numerous flights from Reykjavik, establishing its Icelandic roots.') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Smith, piloting Flight TF-101, took off on a sunny day from Reykjavik.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n),\n\nf_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_2) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.Vehicle_Flight_number, f.Date FROM f_filtered AS f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Reykjavik Airport, identified by RKV, is a central airport in Iceland.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'As an Icelandic airline, Icelandair operates extensively from Reykjavik.') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'On a summer day, Flight TF-101, piloted by Smith, departed from Reykjavik.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n),\n\nf_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_2) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.Vehicle_Flight_number, f.Date FROM f_filtered AS f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'RKV, located in Reykjavik, is a significant airport in Iceland.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Icelandair, an airline based in Iceland, frequently flies from Reykjavik.') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Flight TF-101, under Smith’s command, took off from Reykjavik on a summer day.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n),\n\nf_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_2) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.Vehicle_Flight_number, f.Date FROM f_filtered AS f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 26103 ('MATCH') (line 42, col 39): MATCH [0.03739495947957039, 0.03428329899907112, -0.06795690208673477, -0.052250757813453674, 0.013070731423795223, -0.004874515812844038, -0.002846300834789872. Expected one of: ParserArrayOfJSONIdentifierDelimiter, token sequence, OpeningSquareBracket, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT, INTO OUTFILE, FORMAT, end of query. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE airport (\n `id` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `IATA` Nullable(String),\n `ICAO` Nullable(String),\n `name` Nullable(String),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `id` Nullable(Int64),\n `Vehicle_Flight_number` Nullable(String),\n `Date` Nullable(String),\n `Pilot` Nullable(String),\n `Velocity` Nullable(Float64),\n `Altitude` Nullable(Float64),\n `airport_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);\nCREATE TABLE operate_company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Type` Nullable(String),\n `Principal_activities` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `operate_company_description` Nullable(String),\n `operate_company_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced machine learning techniques for big data analysis') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance\n FROM Student_Enrolment se\n JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country\nFROM Students s\nJOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id)\nJOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "sql_result_column_count": 6, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Where can I find the addresses of those students enrolled in some top programs focused on advanced machine learning for big data?", + "external_knowledge": "The `MATCH` operator performs an approximate nearest neighbor (ANN) search to find the most similar items based on vector embeddings. The embedding function `lembed('all-MiniLM-L6-v2', \"Advanced machine learning techniques for big data analysis\") creates a vector representation of the specified topic, which is then compared against the degree program summaries. The `k=5` condition restricts the results to the top 5 degree programs that have the highest similarity with this vector, using Euclidean distance as the measure of similarity. This approach allows for efficiently identifying degree programs that are most closely aligned with complex topics like machine learning in big data contexts.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-tier programs in machine learning for large-scale data') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced courses in machine learning and data analytics') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading programs for machine learning in big data environments') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Machine learning specialization for big data challenges') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Elite programs in advanced machine learning for extensive data analysis') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "car_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'luxury car') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId\n FROM continents c\n JOIN countries co ON toString(c.ContId) = toString(co.Continent)\n WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker\n FROM car_makers cm\n JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance\nFROM model_list ml\nJOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Top 3 luxury car models from European manufacturers.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'top luxury European cars') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'high-end European car models') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'premium European automobiles') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'luxury European vehicle models') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'exclusive European cars') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE car_makers (\n `Id` Nullable(Int64),\n `Maker` Nullable(String),\n `FullName` Nullable(String),\n `Country` Nullable(String),\n `car_makers_description` Nullable(String),\n `car_makers_description_embedding` Array(Float32)\n);\nCREATE TABLE car_names (\n `MakeId` Nullable(Int64),\n `Model` Nullable(String),\n `Make` Nullable(String),\n `car_names_description` Nullable(String),\n `car_names_description_embedding` Array(Float32)\n);\nCREATE TABLE cars_data (\n `Id` Nullable(Int64),\n `MPG` Nullable(String),\n `Cylinders` Nullable(Int64),\n `Edispl` Nullable(Float64),\n `Horsepower` Nullable(String),\n `Weight` Nullable(Int64),\n `Accelerate` Nullable(Float64),\n `Year` Nullable(Int64),\n `cars_data_description` Nullable(String),\n `cars_data_description_embedding` Array(Float32)\n);\nCREATE TABLE continents (\n `ContId` Nullable(Int64),\n `Continent` Nullable(String),\n `continents_description` Nullable(String)\n);\nCREATE TABLE countries (\n `CountryId` Nullable(Int64),\n `CountryName` Nullable(String),\n `Continent` Nullable(Int64),\n `countries_description` Nullable(String),\n `countries_description_embedding` Array(Float32)\n);\nCREATE TABLE model_list (\n `ModelId` Nullable(Int64),\n `Maker` Nullable(Int64),\n `Model` Nullable(String),\n `model_list_description` Nullable(String),\n `model_list_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "car_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A country known for its automotive industry and economic strength.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance\n FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description\n FROM car_makers cm\n JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId\nFROM CarMakerCountryJoin cmc\nORDER BY cmc.CarMakerId;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to find the IDs of car makers situated in the top 5 countries recognized for their automotive industry and economic strength. These countries should be identified based on a vector similarity search and the results must be sorted by car maker IDs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Countries leading in automotive manufacturing and economic prowess.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Nations excelling in car production and financial stability.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top countries for automotive industry and economic influence.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading nations in car industry and economic power.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Countries recognized for automotive sector and economic dominance.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE car_makers (\n `Id` Nullable(Int64),\n `Maker` Nullable(String),\n `FullName` Nullable(String),\n `Country` Nullable(String),\n `car_makers_description` Nullable(String),\n `car_makers_description_embedding` Array(Float32)\n);\nCREATE TABLE car_names (\n `MakeId` Nullable(Int64),\n `Model` Nullable(String),\n `Make` Nullable(String),\n `car_names_description` Nullable(String),\n `car_names_description_embedding` Array(Float32)\n);\nCREATE TABLE cars_data (\n `Id` Nullable(Int64),\n `MPG` Nullable(String),\n `Cylinders` Nullable(Int64),\n `Edispl` Nullable(Float64),\n `Horsepower` Nullable(String),\n `Weight` Nullable(Int64),\n `Accelerate` Nullable(Float64),\n `Year` Nullable(Int64),\n `cars_data_description` Nullable(String),\n `cars_data_description_embedding` Array(Float32)\n);\nCREATE TABLE continents (\n `ContId` Nullable(Int64),\n `Continent` Nullable(String),\n `continents_description` Nullable(String)\n);\nCREATE TABLE countries (\n `CountryId` Nullable(Int64),\n `CountryName` Nullable(String),\n `Continent` Nullable(Int64),\n `countries_description` Nullable(String),\n `countries_description_embedding` Array(Float32)\n);\nCREATE TABLE model_list (\n `ModelId` Nullable(Int64),\n `Maker` Nullable(Int64),\n `Model` Nullable(String),\n `model_list_description` Nullable(String),\n `model_list_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced topics in technology and innovation') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance\nFROM Degree_Programs dp\nJOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "What are the names of the degree programs and their corresponding department names that most align with advanced topics in technology and innovation? Can you provide the top 5 matches?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge technology and innovation studies') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Programs focusing on technology advancements and innovation') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Technology and innovation-focused degree programs') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovation and advanced technology programs') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Degrees in technology innovation and advancements') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "student_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'This teacher is excellent at interactive teaching methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance\n FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName\nFROM list l\nJOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom)\nORDER BY mt.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you find me the last names of the top 10 people from classrooms with the best 3 teachers who are really great at interactive teaching methods? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'This teacher excels in engaging students through interactive methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding teacher known for interactive teaching techniques.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly effective teacher using interactive teaching strategies.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Teacher skilled in interactive learning methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Interactive teaching expert with great methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE list (\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Grade` Nullable(Int64),\n `Classroom` Nullable(Int64),\n `list_description` Nullable(String),\n `list_description_embedding` Array(Float32)\n);\nCREATE TABLE teachers (\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Classroom` Nullable(Int64),\n `teachers_description` Nullable(String),\n `teachers_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "dog_kennels", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An abandoned dog who found a new home') AS ref_vec_0\n\nSELECT \n d.name AS dog_name, \n b.breed_name AS breed_name, \n AVG(t.cost_of_treatment) AS avg_treatment_cost, distance(d.Dogs_description_embedding, ref_vec_0) AS distance\nFROM \n Dogs d\nJOIN \n Breeds b ON toString(d.breed_code) = toString(b.breed_code)\nJOIN \n Treatments t ON toString(d.dog_id) = toString(t.dog_id)\n \n \nGROUP BY \n d.name, b.breed_name\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the names and breeds of the top 3 dogs that are most representative of the idea of an abandoned dog who found a new home, along with their average treatment costs? Please prioritize those that received treatment the most recently.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Rescued dogs thriving in new homes') AS ref_vec_0\n\nSELECT d.name AS dog_name, b.breed_name, AVG(t.cost_of_treatment) AS avg_treatment_cost, distance(d.Dogs_description_embedding, ref_vec_0) AS distance FROM Dogs d JOIN Breeds b ON toString(d.breed_code) = toString(b.breed_code) JOIN Treatments t ON toString(d.dog_id) = toString(t.dog_id) GROUP BY d.name, b.breed_name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dogs that found loving families after rescue') AS ref_vec_0\n\nSELECT d.name AS dog_name, b.breed_name, AVG(t.cost_of_treatment) AS avg_treatment_cost, distance(d.Dogs_description_embedding, ref_vec_0) AS distance FROM Dogs d JOIN Breeds b ON toString(d.breed_code) = toString(b.breed_code) JOIN Treatments t ON toString(d.dog_id) = toString(t.dog_id) GROUP BY d.name, b.breed_name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Stray dogs adopted into caring homes') AS ref_vec_0\n\nSELECT d.name AS dog_name, b.breed_name, AVG(t.cost_of_treatment) AS avg_treatment_cost, distance(d.Dogs_description_embedding, ref_vec_0) AS distance FROM Dogs d JOIN Breeds b ON toString(d.breed_code) = toString(b.breed_code) JOIN Treatments t ON toString(d.dog_id) = toString(t.dog_id) GROUP BY d.name, b.breed_name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Homeless dogs given a second chance') AS ref_vec_0\n\nSELECT d.name AS dog_name, b.breed_name, AVG(t.cost_of_treatment) AS avg_treatment_cost, distance(d.Dogs_description_embedding, ref_vec_0) AS distance FROM Dogs d JOIN Breeds b ON toString(d.breed_code) = toString(b.breed_code) JOIN Treatments t ON toString(d.dog_id) = toString(t.dog_id) GROUP BY d.name, b.breed_name\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dogs rescued from abandonment now in homes') AS ref_vec_0\n\nSELECT d.name AS dog_name, b.breed_name, AVG(t.cost_of_treatment) AS avg_treatment_cost, distance(d.Dogs_description_embedding, ref_vec_0) AS distance FROM Dogs d JOIN Breeds b ON toString(d.breed_code) = toString(b.breed_code) JOIN Treatments t ON toString(d.dog_id) = toString(t.dog_id) GROUP BY d.name, b.breed_name\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'Dogs_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Breeds (\n `breed_code` Nullable(String),\n `breed_name` Nullable(String),\n `Breeds_description` Nullable(String),\n `Breeds_description_embedding` Array(Float32)\n);\nCREATE TABLE Charges (\n `charge_id` Nullable(Int64),\n `charge_type` Nullable(String),\n `charge_amount` Nullable(Float64),\n `Charges_description` Nullable(String),\n `Charges_description_embedding` Array(Float32)\n);\nCREATE TABLE Dogs (\n `dog_id` Nullable(Int64),\n `owner_id` Nullable(Int64),\n `abandoned_yn` Nullable(String),\n `breed_code` Nullable(String),\n `size_code` Nullable(String),\n `name` Nullable(String),\n `age` Nullable(String),\n `date_of_birth` Nullable(String),\n `gender` Nullable(String),\n `weight` Nullable(String),\n `date_arrived` Nullable(String),\n `date_adopted` Nullable(String),\n `date_departed` Nullable(String),\n `Dogs_description` Nullable(String),\n `Dogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Owners (\n `owner_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `street` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `zip_code` Nullable(String),\n `email_address` Nullable(String),\n `home_phone` Nullable(String),\n `cell_number` Nullable(String),\n `Owners_description` Nullable(String),\n `Owners_description_embedding` Array(Float32)\n);\nCREATE TABLE Professionals (\n `professional_id` Nullable(Int64),\n `role_code` Nullable(String),\n `first_name` Nullable(String),\n `street` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `zip_code` Nullable(String),\n `last_name` Nullable(String),\n `email_address` Nullable(String),\n `home_phone` Nullable(String),\n `cell_number` Nullable(String),\n `Professionals_description` Nullable(String),\n `Professionals_description_embedding` Array(Float32)\n);\nCREATE TABLE Sizes (\n `size_code` Nullable(String),\n `size_description` Nullable(String)\n);\nCREATE TABLE Treatment_Types (\n `treatment_type_code` Nullable(String),\n `treatment_type_description` Nullable(String),\n `treatment_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Treatments (\n `treatment_id` Nullable(Int64),\n `dog_id` Nullable(Int64),\n `professional_id` Nullable(Int64),\n `treatment_type_code` Nullable(String),\n `date_of_treatment` Nullable(String),\n `cost_of_treatment` Nullable(Float64),\n `Treatments_description` Nullable(String),\n `Treatments_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "customer_complaints", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A new range of eco-friendly furniture') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance \nFROM Products\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "What is the name of the product that most likely fits the idea of brand-new eco-friendly furniture?", + "external_knowledge": "In vector searches using the SQLite extension \"sqlite-lembed,\" the MATCH operator facilitates an approximate nearest neighbor (ANN) search. This process finds the closest vectors to a given input by calculating Euclidean distances, where smaller distances indicate higher similarity. The `lembed('all-MiniLM-L6-v2', ...)` function converts text phrases into vector embeddings using a pre-trained model, enabling the database to perform semantic comparisons rather than exact matches. The `LIMIT 1` clause ensures that only the most relevant result is returned, focusing on the top match for the specified concept.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative eco-friendly furniture collection') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Brand-new green furniture line') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sustainable and modern furniture') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Environmentally conscious furniture designs') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Eco-friendly and stylish furniture options') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Complaints (\n `complaint_id` Nullable(Int64),\n `product_id` Nullable(Int64),\n `customer_id` Nullable(Int64),\n `complaint_outcome_code` Nullable(String),\n `complaint_status_code` Nullable(String),\n `complaint_type_code` Nullable(String),\n `date_complaint_raised` Nullable(String),\n `date_complaint_closed` Nullable(String),\n `staff_id` Nullable(Int64),\n `Complaints_description` Nullable(String),\n `Complaints_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_type_code` Nullable(String),\n `address_line_1` Nullable(String),\n `address_line_2` Nullable(String),\n `town_city` Nullable(String),\n `state` Nullable(String),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `parent_product_id` Nullable(Int64),\n `product_category_code` Nullable(String),\n `date_product_first_available` Nullable(String),\n `date_product_discontinued` Nullable(String),\n `product_name` Nullable(String),\n `product_description` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "department_store", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'High-quality jeans for summer') AS ref_vec_0,\n\nProductMatches AS (\n SELECT \n p.product_id AS product_id,\n p.product_name AS product_name,\n ps.supplier_id AS supplier_id,\n p.Products_description_embedding AS Products_description_embedding,\n distance(p.Products_description_embedding, ref_vec_0) AS distance\n FROM \n Products p\n JOIN \n Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT \n pm.product_name AS product_name,\n s.supplier_name AS supplier_name,\n pm.distance AS distance\n FROM \n ProductMatches pm\n JOIN\n Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT\n product_name,\n supplier_name\nFROM \n SupplierInfo\nORDER BY \n distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "**\n\nPlease provide the names of the top 5 suppliers who offer products most closely resembling high-quality jeans for summer, along with the names of these products.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Premium summer jeans') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-quality summer denim') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best summer jeans') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-end summer denim') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quality summer jeans') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Addresses_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customer_Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status_code` String,\n `order_date` Date\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `customer_code` Nullable(String),\n `customer_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain (\n `dept_store_chain_id` Nullable(Int64),\n `dept_store_chain_name` Nullable(String),\n `Department_Store_Chain_description` Nullable(String),\n `Department_Store_Chain_description_embedding` Array(Float32)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Department_Stores (\n `dept_store_id` Nullable(Int64),\n `dept_store_chain_id` Nullable(Int64),\n `store_name` Nullable(String),\n `store_address` Nullable(String),\n `store_phone` Nullable(String),\n `store_email` Nullable(String),\n `Department_Stores_description` Nullable(String),\n `Department_Stores_description_embedding` Array(Float32)\n);\nCREATE TABLE Department_Stores_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `dept_store_id` Nullable(Int64),\n `department_name` Nullable(String),\n `Departments_description` Nullable(String),\n `Departments_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Nullable(Int64),\n `order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Product_Suppliers (\n `product_id` Int64,\n `supplier_id` Int64,\n `date_supplied_from` Date,\n `date_supplied_to` Nullable(Date),\n `total_amount_purchased` Nullable(String),\n `total_value_purchased` Nullable(Decimal(38, 6))\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_type_code` Nullable(String),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_gender` Nullable(String),\n `staff_name` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Department_Assignments (\n `staff_id` Int64,\n `department_id` Int64,\n `date_assigned_from` Date,\n `job_title_code` String,\n `date_assigned_to` Nullable(Date)\n);\nCREATE TABLE Staff_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Supplier_Addresses (\n `supplier_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Suppliers (\n `supplier_id` Nullable(Int64),\n `supplier_name` Nullable(String),\n `supplier_phone` Nullable(String),\n `Suppliers_description` Nullable(String),\n `Suppliers_description_embedding` Array(Float32)\n);\nCREATE TABLE Suppliers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "phone_market", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Affordable smartphone with good memory and camera features available in various carriers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Highly ranked market with numerous shops and employees') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(phone_description_embedding, ref_vec_0) AS distance\n FROM PhoneVectorSearch\n WHERE phone_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Affordable smartphone with good memory AND camera features available in various carriers')\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM MarketVectorSearch\n WHERE market_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Highly ranked market with numerous shops AND employees')\n ORDER BY distance\n LIMIT 2\n),\n\nPhoneVectorSearch AS (\n SELECT \n p.Phone_ID AS Phone_ID, \n p.Name AS Name, \n pm.Market_ID AS Market_ID, \n distance \n FROM p_filtered AS p \n JOIN \n phone_market pm ON toString(p.Phone_ID) = toString(pm.Phone_ID)\n),\n\nMarketVectorSearch AS (\n SELECT \n m.Market_ID AS Market_ID, \n m.District AS District, \n distance \n FROM m_filtered AS m\n)\n\nSELECT \n p.Phone_ID AS Phone_ID, \n m.District AS District \nFROM \n PhoneVectorSearch p \nJOIN \n MarketVectorSearch m ON toString(p.Market_ID) = toString(m.Market_ID) \nORDER BY \n m.distance AS distance, \n p.distance AS distance \nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the top 5 affordable smartphones with awesome memory and camera features available in multiple carriers? I'd like to know their IDs and which district has those highly ranked markets with loads of shops and employees.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Budget-friendly smartphones with excellent memory and camera across various networks') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top markets with plenty of stores and staff') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(phone_description_embedding, ref_vec_0) AS distance\n FROM PhoneVectorSearch\n WHERE phone_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Budget-friendly smartphones with excellent memory AND camera across various networks')\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM MarketVectorSearch\n WHERE market_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Top markets with plenty of stores AND staff')\n ORDER BY distance\n LIMIT 2\n),\n\nPhoneVectorSearch AS (\n SELECT p.Phone_ID, p.Name, pm.Market_ID, distance FROM p_filtered AS p JOIN phone_market pm ON toString(p.Phone_ID) = toString(pm.Phone_ID)\n),\n\nMarketVectorSearch AS (\n SELECT m.Market_ID, m.District, distance FROM m_filtered AS m\n)\n\nSELECT p.Phone_ID, m.District FROM PhoneVectorSearch p JOIN MarketVectorSearch m ON toString(p.Market_ID) = toString(m.Market_ID) ORDER BY m.distance, p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Affordable smartphones with strong memory and camera features across multiple carriers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Highly rated markets with many shops and employees') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(phone_description_embedding, ref_vec_0) AS distance\n FROM PhoneVectorSearch\n WHERE phone_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Affordable smartphones with strong memory AND camera features across multiple carriers')\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM MarketVectorSearch\n WHERE market_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Highly rated markets with many shops AND employees')\n ORDER BY distance\n LIMIT 2\n),\n\nPhoneVectorSearch AS (\n SELECT p.Phone_ID, p.Name, pm.Market_ID, distance FROM p_filtered AS p JOIN phone_market pm ON toString(p.Phone_ID) = toString(pm.Phone_ID)\n),\n\nMarketVectorSearch AS (\n SELECT m.Market_ID, m.District, distance FROM m_filtered AS m\n)\n\nSELECT p.Phone_ID, m.District FROM PhoneVectorSearch p JOIN MarketVectorSearch m ON toString(p.Market_ID) = toString(m.Market_ID) ORDER BY m.distance, p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Cost-effective phones with great memory and camera options available through different carriers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Markets with high rankings and lots of stores and personnel') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(phone_description_embedding, ref_vec_0) AS distance\n FROM PhoneVectorSearch\n WHERE phone_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Cost-effective phones with great memory AND camera options available through different carriers')\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM MarketVectorSearch\n WHERE market_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Markets with high rankings AND lots of stores AND personnel')\n ORDER BY distance\n LIMIT 2\n),\n\nPhoneVectorSearch AS (\n SELECT p.Phone_ID, p.Name, pm.Market_ID, distance FROM p_filtered AS p JOIN phone_market pm ON toString(p.Phone_ID) = toString(pm.Phone_ID)\n),\n\nMarketVectorSearch AS (\n SELECT m.Market_ID, m.District, distance FROM m_filtered AS m\n)\n\nSELECT p.Phone_ID, m.District FROM PhoneVectorSearch p JOIN MarketVectorSearch m ON toString(p.Market_ID) = toString(m.Market_ID) ORDER BY m.distance, p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Economical smartphones with superb memory and camera features across several carriers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Prominent markets with a variety of shops and employees') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(phone_description_embedding, ref_vec_0) AS distance\n FROM PhoneVectorSearch\n WHERE phone_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Economical smartphones with superb memory AND camera features across several carriers')\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM MarketVectorSearch\n WHERE market_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Prominent markets with a variety of shops AND employees')\n ORDER BY distance\n LIMIT 2\n),\n\nPhoneVectorSearch AS (\n SELECT p.Phone_ID, p.Name, pm.Market_ID, distance FROM p_filtered AS p JOIN phone_market pm ON toString(p.Phone_ID) = toString(pm.Phone_ID)\n),\n\nMarketVectorSearch AS (\n SELECT m.Market_ID, m.District, distance FROM m_filtered AS m\n)\n\nSELECT p.Phone_ID, m.District FROM PhoneVectorSearch p JOIN MarketVectorSearch m ON toString(p.Market_ID) = toString(m.Market_ID) ORDER BY m.distance, p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Low-cost smartphones with impressive memory and camera features available on multiple networks') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Leading markets with numerous shops and staff') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(phone_description_embedding, ref_vec_0) AS distance\n FROM PhoneVectorSearch\n WHERE phone_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Low-cost smartphones with impressive memory AND camera features available on multiple networks')\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(market_description_embedding, ref_vec_1) AS distance\n FROM MarketVectorSearch\n WHERE market_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Leading markets with numerous shops AND staff')\n ORDER BY distance\n LIMIT 2\n),\n\nPhoneVectorSearch AS (\n SELECT p.Phone_ID, p.Name, pm.Market_ID, distance FROM p_filtered AS p JOIN phone_market pm ON toString(p.Phone_ID) = toString(pm.Phone_ID)\n),\n\nMarketVectorSearch AS (\n SELECT m.Market_ID, m.District, distance FROM m_filtered AS m\n)\n\nSELECT p.Phone_ID, m.District FROM PhoneVectorSearch p JOIN MarketVectorSearch m ON toString(p.Market_ID) = toString(m.Market_ID) ORDER BY m.distance, p.distance LIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 17064 ('MATCH') (line 10, col 39): MATCH [-0.0027699789498001337, 0.07865874469280243, 0.06103774532675743, -0.0030665781814604998, 0.009053174406290054, 0.0280993040651083, 0.01667313277721405, . Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE market (\n `Market_ID` Nullable(Int64),\n `District` Nullable(String),\n `Num_of_employees` Nullable(Int64),\n `Num_of_shops` Nullable(Float64),\n `Ranking` Nullable(Int64),\n `market_description` Nullable(String),\n `market_description_embedding` Array(Float32)\n);\nCREATE TABLE phone (\n `Name` Nullable(String),\n `Phone_ID` Nullable(Int64),\n `Memory_in_G` Nullable(Int64),\n `Carrier` Nullable(String),\n `Price` Nullable(Float64),\n `phone_description` Nullable(String),\n `phone_description_embedding` Array(Float32)\n);\nCREATE TABLE phone_market (\n `Market_ID` Nullable(Int64),\n `Phone_ID` Nullable(String),\n `Num_of_stock` Nullable(Int64)\n);" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'student interested in computer science and mathematics') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance\nFROM Students\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Reveal the identities and closeness of the top five intellectual explorers whose academic paths align with the realms of numbers and algorithms.", + "external_knowledge": "The \"MATCH\" operator in this query executes an approximate nearest neighbor (ANN) search, identifying items in the dataset that are most similar to a given vector based on specified criteria. The vector generated by the \"lembed\" function converts the text \"student interested in computer science and mathematics\" into a form that can be numerically compared to student embeddings. The \"k = 5\" clause specifies that the query should return the top five results, ordered by similarity. Similarity here is often measured using the Euclidean distance, where a smaller distance indicates a stronger alignment with the specified interests.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'student passionate about numerical analysis and algorithm development') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'academic enthusiast in the fields of computational theory and quantitative studies') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'learner focused on algorithmic structures and mathematical principles') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'explorer of mathematical models and algorithmic processes') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'individual dedicated to the study of algorithms and mathematics') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);" + }, + { + "db_id": "course_teach", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Mathematics course starting in May') AS ref_vec_0\n\nSELECT c.Course_ID, distance(c.course_description_embedding, ref_vec_0) AS distance\nFROM course c\nJOIN course_arrange ca ON toString(c.Course_ID) = toString(ca.Course_ID)\nJOIN teacher t ON toString(ca.Teacher_ID) = toString(t.Teacher_ID)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What is the top course related to \"Advanced Mathematics course starting in May\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Mathematics class beginning in May') AS ref_vec_0\n\nSELECT c.Course_ID, distance(c.course_description_embedding, ref_vec_0) AS distance FROM course c JOIN course_arrange ca ON toString(c.Course_ID) = toString(ca.Course_ID) JOIN teacher t ON toString(ca.Teacher_ID) = toString(t.Teacher_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Math course starting May') AS ref_vec_0\n\nSELECT c.Course_ID, distance(c.course_description_embedding, ref_vec_0) AS distance FROM course c JOIN course_arrange ca ON toString(c.Course_ID) = toString(ca.Course_ID) JOIN teacher t ON toString(ca.Teacher_ID) = toString(t.Teacher_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top Advanced Mathematics course May') AS ref_vec_0\n\nSELECT c.Course_ID, distance(c.course_description_embedding, ref_vec_0) AS distance FROM course c JOIN course_arrange ca ON toString(c.Course_ID) = toString(ca.Course_ID) JOIN teacher t ON toString(ca.Teacher_ID) = toString(t.Teacher_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Mathematics course May start') AS ref_vec_0\n\nSELECT c.Course_ID, distance(c.course_description_embedding, ref_vec_0) AS distance FROM course c JOIN course_arrange ca ON toString(c.Course_ID) = toString(ca.Course_ID) JOIN teacher t ON toString(ca.Teacher_ID) = toString(t.Teacher_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced Mathematics course commencing in May') AS ref_vec_0\n\nSELECT c.Course_ID, distance(c.course_description_embedding, ref_vec_0) AS distance FROM course c JOIN course_arrange ca ON toString(c.Course_ID) = toString(ca.Course_ID) JOIN teacher t ON toString(ca.Teacher_ID) = toString(t.Teacher_ID)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'course_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE course (\n `Course_ID` Nullable(Int64),\n `Staring_Date` Nullable(String),\n `Course` Nullable(String),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_arrange (\n `Course_ID` Nullable(Int64),\n `Teacher_ID` Nullable(Int64),\n `Grade` Nullable(Int64)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE teacher (\n `Teacher_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(String),\n `Hometown` Nullable(String),\n `teacher_description` Nullable(String),\n `teacher_description_embedding` Array(Float32)\n);\nCREATE TABLE teacher_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE teacher_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);" + }, + { + "db_id": "store_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A famous heavy metal band from the 1980s known for their international success') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance \nFROM artists\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the artist who is most representative of a famous heavy metal band from the 1980s known for their international success, and provide their ID along with the similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary heavy metal band from the 1980s with global acclaim') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic 1980s heavy metal band famous worldwide') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned heavy metal group from the 1980s with international fame') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent 1980s heavy metal band celebrated globally') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known heavy metal band from the 1980s with worldwide success') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE albums (\n `id` Nullable(Int64),\n `title` Nullable(String),\n `artist_id` Nullable(Int64),\n `albums_description` Nullable(String),\n `albums_description_embedding` Array(Float32)\n);\nCREATE TABLE artists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `artists_description` Nullable(String),\n `artists_description_embedding` Array(Float32)\n);\nCREATE TABLE customers (\n `id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `company` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `support_rep_id` Nullable(Int64),\n `customers_description` Nullable(String),\n `customers_description_embedding` Array(Float32)\n);\nCREATE TABLE employees (\n `id` Nullable(Int64),\n `last_name` String,\n `first_name` String,\n `title` Nullable(String),\n `reports_to` Nullable(Int64),\n `birth_date` Nullable(String),\n `hire_date` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `employees_description` Nullable(String)\n);\nCREATE TABLE genres (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `genres_description` Nullable(String),\n `genres_description_embedding` Array(Float32)\n);\nCREATE TABLE invoice_lines (\n `id` Nullable(Int64),\n `invoice_id` Int64,\n `track_id` Int64,\n `unit_price` Decimal(38, 6),\n `quantity` Int64\n);\nCREATE TABLE invoices (\n `id` Nullable(Int64),\n `customer_id` Int64,\n `invoice_date` String,\n `billing_address` Nullable(String),\n `billing_city` Nullable(String),\n `billing_state` Nullable(String),\n `billing_country` Nullable(String),\n `billing_postal_code` Nullable(String),\n `total` Decimal(38, 6),\n `invoices_description` Nullable(String)\n);\nCREATE TABLE media_types (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `media_types_description` Nullable(String),\n `media_types_description_embedding` Array(Float32)\n);\nCREATE TABLE playlist_tracks (\n `playlist_id` Int64,\n `track_id` Int64\n);\nCREATE TABLE playlists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `playlists_description` Nullable(String),\n `playlists_description_embedding` Array(Float32)\n);\nCREATE TABLE tracks (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `album_id` Nullable(Int64),\n `media_type_id` Nullable(Int64),\n `genre_id` Nullable(Int64),\n `composer` Nullable(String),\n `milliseconds` Nullable(Int64),\n `bytes` Nullable(Int64),\n `unit_price` Nullable(Float64),\n `tracks_description` Nullable(String),\n `tracks_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "election", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party fielded a strong lineup for the major state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance \nFROM party\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Could you identify which political party is most closely associated with having a strong lineup for major state positions in 2020, as per the description provided? Please return only the top match.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party was noted for its strong candidates in key state roles.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Republican party in 2020 had a formidable lineup for major state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party was recognized for having a strong presence in significant state roles.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Republican party in 2020 assembled a strong roster for important state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party was associated with a robust lineup for key state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE county (\n `County_Id` Nullable(Int64),\n `County_name` Nullable(String),\n `Population` Nullable(Float64),\n `Zip_code` Nullable(String),\n `county_description` Nullable(String),\n `county_description_embedding` Array(Float32)\n);\nCREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Counties_Represented` Nullable(String),\n `District` Nullable(Int64),\n `Delegate` Nullable(String),\n `Party` Nullable(Int64),\n `First_Elected` Nullable(Float64),\n `Committee` Nullable(String),\n `election_description` Nullable(String),\n `election_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Party` Nullable(String),\n `Governor` Nullable(String),\n `Lieutenant_Governor` Nullable(String),\n `Comptroller` Nullable(String),\n `Attorney_General` Nullable(String),\n `US_Senate` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "coffee_shop", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A coffee shop located at 123 Elm Street with 10 staff members, a score of 45.5, and opened in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance \nFROM shop\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the Shop_ID and its similarity score for the coffee shop that most closely matches the description of being located at 123 Elm Street, having 10 staff members, a score of 45.5, and opened in 2015?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Coffee shop at 123 Elm Street, staffed by 10 people, rated 45.5, opened in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Located at 123 Elm Street, this coffee shop employs 10 staff, has a score of 45.5, and started operations in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A coffee shop near 123 Elm Street, with 10 employees, a rating of 45.5, and established in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Coffee shop situated at 123 Elm Street, with 10 staff members, a score of 45.5, and opened in the year 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Shop at 123 Elm Street, featuring 10 staff, a score of 45.5, and opened in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE happy_hour (\n `HH_ID` Nullable(Int64),\n `Shop_ID` Nullable(Int64),\n `Month` Nullable(String),\n `Num_of_shaff_in_charge` Nullable(Int64)\n);\nCREATE TABLE happy_hour_member (\n `HH_ID` Nullable(Int64),\n `Member_ID` Nullable(Int64),\n `Total_amount` Nullable(Float64)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Membership_card` Nullable(String),\n `Age` Nullable(Int64),\n `Time_of_purchase` Nullable(Int64),\n `Level_of_membership` Nullable(Int64),\n `Address` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Address` Nullable(String),\n `Num_of_staff` Nullable(String),\n `Score` Nullable(Float64),\n `Open_Year` Nullable(String),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "match_season", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A player with a short career in 2011, having no match outcomes recorded.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance\nFROM player\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you find five players who had brief careers around 2011 and didn't have recorded match outcomes?", + "external_knowledge": "The `MATCH` operator along with the `lembed` function performs a vector similarity search, known as approximate nearest neighbor (ANN) search. This operation identifies items resembling a specified vector phrase, ranking them by similarity. The `k = 5` parameter indicates that the search returns the five closest matches. Similarity is assessed using Euclidean distance (L2 norm), where lower distance values reflect higher similarity. The `lembed` model, `all-MiniLM-L6-v2`, is used to generate meaningful embeddings of text descriptions to facilitate this comparison.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Players with brief stints in 2011 and no match results.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Individuals with short playing periods in 2011, lacking match records.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Athletes who played only briefly in 2011 without any match outcomes.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players with limited careers during 2011 and missing match data.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Competitors with short 2011 careers without recorded match results.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 4, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_id` Nullable(Int64),\n `Country_name` Nullable(String),\n `Capital` Nullable(String),\n `Official_native_language` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE match_season (\n `Season` Nullable(Float64),\n `Player` Nullable(String),\n `Position` Nullable(String),\n `Country` Nullable(Int64),\n `Team` Nullable(Int64),\n `Draft_Pick_Number` Nullable(Int64),\n `Draft_Class` Nullable(String),\n `College` Nullable(String),\n `match_season_description` Nullable(String),\n `match_season_description_embedding` Array(Float32)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Years_Played` Nullable(String),\n `Total_WL` Nullable(String),\n `Singles_WL` Nullable(String),\n `Doubles_WL` Nullable(String),\n `Team` Nullable(Int64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE team (\n `Team_id` Nullable(Int64),\n `Name` Nullable(String),\n `team_description` Nullable(String),\n `team_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "match_season", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An outstanding player with a remarkable performance record in singles matches.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance\nFROM player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the player who is considered an outstanding performer in singles matches based on their playing record?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A player with exceptional singles match achievements.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A singles match performer with outstanding records.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top performer known for singles success.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An athlete with impressive singles match history.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A player distinguished by excellent singles performance.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_id` Nullable(Int64),\n `Country_name` Nullable(String),\n `Capital` Nullable(String),\n `Official_native_language` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE match_season (\n `Season` Nullable(Float64),\n `Player` Nullable(String),\n `Position` Nullable(String),\n `Country` Nullable(Int64),\n `Team` Nullable(Int64),\n `Draft_Pick_Number` Nullable(Int64),\n `Draft_Class` Nullable(String),\n `College` Nullable(String),\n `match_season_description` Nullable(String),\n `match_season_description_embedding` Array(Float32)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Years_Played` Nullable(String),\n `Total_WL` Nullable(String),\n `Singles_WL` Nullable(String),\n `Doubles_WL` Nullable(String),\n `Team` Nullable(Int64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE team (\n `Team_id` Nullable(Int64),\n `Name` Nullable(String),\n `team_description` Nullable(String),\n `team_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "gymnast", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnast scoring high in parallel bars and horizontal bar') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Young athlete originally from a small town') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Gymnast scoring high in parallel bars\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n g.Gymnast_ID AS Gymnast_ID,\n g.Total_Points AS Total_Points,\n g.Floor_Exercise_Points AS Floor_Exercise_Points,\n g.Horizontal_Bar_Points AS Horizontal_Bar_Points,\n p.Name AS Name,\n p.Age AS Age,\n g.distance AS gymnast_distance\nFROM g_filtered AS g\nJOIN p_filtered AS p ON toString(p.People_ID) = toString(g.Gymnast_ID)\n WHERE horizontal bar') ORDER BY \n g.distance AS distance\nLIMIT 7;", + "sql_result_column_count": 7, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Seek out the young stars from humble beginnings who shine brightly on the parallel and horizontal bars. Reveal their identities along with the scores that underscore their athletic prowess.", + "external_knowledge": "1. **Vector Search**: The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, essential for identifying entities matching a specific semantic description.\n2. **Embedding Function**: `lembed('all-MiniLM-L6-v2', ...)` uses a language model to create vector representations for textual descriptions, allowing for similarity comparisons based on semantic content.\n3. **Distance Metric**: The `distance` column indicates the level of similarity. Lesser distance implies higher similarity and relevance to the search criteria.\n4. **Domain Context**: \"Gymnast scoring high in parallel bars and horizontal bar\" refers to gymnasts excelling specifically in these events. \"Young athlete originally from a small town\" highlights athletes' background, often associated with unique challenges and achievements.\n5. **Intent**: The search intent is to find gymnasts who, despite coming from small towns, show significant skill in specific athletic disciplines.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'High-scoring gymnasts on parallel and horizontal bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Young talents from modest backgrounds') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'High-scoring gymnasts on parallel\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.Gymnast_ID, g.Total_Points, g.Floor_Exercise_Points, g.Horizontal_Bar_Points, p.Name, p.Age, g.distance AS gymnast_distance FROM g_filtered AS g JOIN p_filtered AS p ON toString(p.People_ID) = toString(g.Gymnast_ID) WHERE horizontal bars') ORDER BY g.distance LIMIT 7;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performers in parallel and horizontal bar events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Young stars from small communities') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Outstanding performers in parallel\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.Gymnast_ID, g.Total_Points, g.Floor_Exercise_Points, g.Horizontal_Bar_Points, p.Name, p.Age, g.distance AS gymnast_distance FROM g_filtered AS g JOIN p_filtered AS p ON toString(p.People_ID) = toString(g.Gymnast_ID) WHERE horizontal bar events') ORDER BY g.distance LIMIT 7;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top gymnasts excelling in parallel bars and horizontal bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Promising athletes from rural areas') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Top gymnasts excelling in parallel bars\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.Gymnast_ID, g.Total_Points, g.Floor_Exercise_Points, g.Horizontal_Bar_Points, p.Name, p.Age, g.distance AS gymnast_distance FROM g_filtered AS g JOIN p_filtered AS p ON toString(p.People_ID) = toString(g.Gymnast_ID) WHERE horizontal bars') ORDER BY g.distance LIMIT 7;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnasts with high scores in parallel and horizontal bar routines') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Young athletes from humble origins') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Gymnasts with high scores in parallel\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.Gymnast_ID, g.Total_Points, g.Floor_Exercise_Points, g.Horizontal_Bar_Points, p.Name, p.Age, g.distance AS gymnast_distance FROM g_filtered AS g JOIN p_filtered AS p ON toString(p.People_ID) = toString(g.Gymnast_ID) WHERE horizontal bar routines') ORDER BY g.distance LIMIT 7;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Elite gymnasts in parallel bars and horizontal bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Young competitors from less privileged areas') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Elite gymnasts in parallel bars\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.Gymnast_ID, g.Total_Points, g.Floor_Exercise_Points, g.Horizontal_Bar_Points, p.Name, p.Age, g.distance AS gymnast_distance FROM g_filtered AS g JOIN p_filtered AS p ON toString(p.People_ID) = toString(g.Gymnast_ID) WHERE horizontal bars') ORDER BY g.distance LIMIT 7;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 16946 ('(') (line 5, col 15): (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n WHERE gymnast_description_embedding MATCH le. Unmatched parentheses: (. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE gymnast (\n `Gymnast_ID` Nullable(Int64),\n `Floor_Exercise_Points` Nullable(Float64),\n `Pommel_Horse_Points` Nullable(Float64),\n `Rings_Points` Nullable(Float64),\n `Vault_Points` Nullable(Float64),\n `Parallel_Bars_Points` Nullable(Float64),\n `Horizontal_Bar_Points` Nullable(Float64),\n `Total_Points` Nullable(Float64),\n `gymnast_description` Nullable(String),\n `gymnast_description_embedding` Array(Float32)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Float64),\n `Height` Nullable(Float64),\n `Hometown` Nullable(String),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "store_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary rock band with timeless hits and energetic performances.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance\nFROM artists\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you help me find the id of an artist who's like a legendary rock band with timeless hits and energetic performances?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic rock band known for its ageless songs and dynamic stage presence.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned rock group with classic tracks and high-energy shows.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A famous rock band celebrated for its enduring music and lively performances.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary rock ensemble with unforgettable hits and vibrant live acts.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known rock band with timeless songs and electrifying concerts.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE albums (\n `id` Nullable(Int64),\n `title` Nullable(String),\n `artist_id` Nullable(Int64),\n `albums_description` Nullable(String),\n `albums_description_embedding` Array(Float32)\n);\nCREATE TABLE artists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `artists_description` Nullable(String),\n `artists_description_embedding` Array(Float32)\n);\nCREATE TABLE customers (\n `id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `company` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `support_rep_id` Nullable(Int64),\n `customers_description` Nullable(String),\n `customers_description_embedding` Array(Float32)\n);\nCREATE TABLE employees (\n `id` Nullable(Int64),\n `last_name` String,\n `first_name` String,\n `title` Nullable(String),\n `reports_to` Nullable(Int64),\n `birth_date` Nullable(String),\n `hire_date` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `employees_description` Nullable(String)\n);\nCREATE TABLE genres (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `genres_description` Nullable(String),\n `genres_description_embedding` Array(Float32)\n);\nCREATE TABLE invoice_lines (\n `id` Nullable(Int64),\n `invoice_id` Int64,\n `track_id` Int64,\n `unit_price` Decimal(38, 6),\n `quantity` Int64\n);\nCREATE TABLE invoices (\n `id` Nullable(Int64),\n `customer_id` Int64,\n `invoice_date` String,\n `billing_address` Nullable(String),\n `billing_city` Nullable(String),\n `billing_state` Nullable(String),\n `billing_country` Nullable(String),\n `billing_postal_code` Nullable(String),\n `total` Decimal(38, 6),\n `invoices_description` Nullable(String)\n);\nCREATE TABLE media_types (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `media_types_description` Nullable(String),\n `media_types_description_embedding` Array(Float32)\n);\nCREATE TABLE playlist_tracks (\n `playlist_id` Int64,\n `track_id` Int64\n);\nCREATE TABLE playlists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `playlists_description` Nullable(String),\n `playlists_description_embedding` Array(Float32)\n);\nCREATE TABLE tracks (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `album_id` Nullable(Int64),\n `media_type_id` Nullable(Int64),\n `genre_id` Nullable(Int64),\n `composer` Nullable(String),\n `milliseconds` Nullable(Int64),\n `bytes` Nullable(Int64),\n `unit_price` Nullable(Float64),\n `tracks_description` Nullable(String),\n `tracks_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "gymnast", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnast with a strong performance in parallel bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A person from Santo Domingo') AS ref_vec_1,\n\ngymnast_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGymnasts AS (\n SELECT \n Gymnast_ID, \n Total_Points, \n distance AS gymnast_distance\n FROM gymnast_filtered AS gymnast\n),\n\nSimilarPeople AS (\n SELECT \n People_ID,\n Name,\n Age,\n hometown,\n distance AS people_distance\n FROM people_filtered AS people\n)\n\nSELECT \n sg.Gymnast_ID AS Gymnast_ID,\n sp.People_ID AS People_ID,\n sg.Total_Points AS Total_Points,\n sp.Name AS Name,\n sp.Age AS Age,\n sg.gymnast_distance AS gymnast_distance,\n sp.people_distance AS people_distance\nFROM \n SimilarGymnasts sg\nJOIN \n SimilarPeople sp ON toString(sg.Gymnast_ID) = toString(sp.People_ID)\nORDER BY \n sg.gymnast_distance + sp.people_distance\nLIMIT 10;", + "sql_result_column_count": 7, + "sql_result_rows_count": 2, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Return the top 10 gymnasts from the top 5 with strong parallel bars performance and people from the top 5 from Santo Domingo. Include their IDs, total points, names, ages, and distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnast excelling in parallel bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Resident of Santo Domingo') AS ref_vec_1,\n\ngymnast_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGymnasts AS (\n SELECT Gymnast_ID, Total_Points, distance AS gymnast_distance FROM gymnast_filtered AS gymnast\n),\n\nSimilarPeople AS (\n SELECT People_ID, Name, Age, hometown, distance AS people_distance FROM people_filtered AS people\n)\n\nSELECT sg.Gymnast_ID, sp.People_ID, sg.Total_Points, sp.Name, sp.Age, sg.gymnast_distance, sp.people_distance FROM SimilarGymnasts sg JOIN SimilarPeople sp ON toString(sg.Gymnast_ID) = toString(sp.People_ID) ORDER BY sg.gymnast_distance + sp.people_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnast with high scores on parallel bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Individual from Santo Domingo') AS ref_vec_1,\n\ngymnast_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGymnasts AS (\n SELECT Gymnast_ID, Total_Points, distance AS gymnast_distance FROM gymnast_filtered AS gymnast\n),\n\nSimilarPeople AS (\n SELECT People_ID, Name, Age, hometown, distance AS people_distance FROM people_filtered AS people\n)\n\nSELECT sg.Gymnast_ID, sp.People_ID, sg.Total_Points, sp.Name, sp.Age, sg.gymnast_distance, sp.people_distance FROM SimilarGymnasts sg JOIN SimilarPeople sp ON toString(sg.Gymnast_ID) = toString(sp.People_ID) ORDER BY sg.gymnast_distance + sp.people_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Athlete with parallel bars expertise') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Person originating from Santo Domingo') AS ref_vec_1,\n\ngymnast_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGymnasts AS (\n SELECT Gymnast_ID, Total_Points, distance AS gymnast_distance FROM gymnast_filtered AS gymnast\n),\n\nSimilarPeople AS (\n SELECT People_ID, Name, Age, hometown, distance AS people_distance FROM people_filtered AS people\n)\n\nSELECT sg.Gymnast_ID, sp.People_ID, sg.Total_Points, sp.Name, sp.Age, sg.gymnast_distance, sp.people_distance FROM SimilarGymnasts sg JOIN SimilarPeople sp ON toString(sg.Gymnast_ID) = toString(sp.People_ID) ORDER BY sg.gymnast_distance + sp.people_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnast prominent in parallel bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Citizen of Santo Domingo') AS ref_vec_1,\n\ngymnast_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGymnasts AS (\n SELECT Gymnast_ID, Total_Points, distance AS gymnast_distance FROM gymnast_filtered AS gymnast\n),\n\nSimilarPeople AS (\n SELECT People_ID, Name, Age, hometown, distance AS people_distance FROM people_filtered AS people\n)\n\nSELECT sg.Gymnast_ID, sp.People_ID, sg.Total_Points, sp.Name, sp.Age, sg.gymnast_distance, sp.people_distance FROM SimilarGymnasts sg JOIN SimilarPeople sp ON toString(sg.Gymnast_ID) = toString(sp.People_ID) ORDER BY sg.gymnast_distance + sp.people_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Gymnast skilled in parallel bars') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Native of Santo Domingo') AS ref_vec_1,\n\ngymnast_filtered AS (\n SELECT\n *,\n distance(gymnast_description_embedding, ref_vec_0) AS distance\n FROM gymnast\n\n ORDER BY distance\n LIMIT 5\n),\n\npeople_filtered AS (\n SELECT\n *,\n distance(people_description_embedding, ref_vec_1) AS distance\n FROM people\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGymnasts AS (\n SELECT Gymnast_ID, Total_Points, distance AS gymnast_distance FROM gymnast_filtered AS gymnast\n),\n\nSimilarPeople AS (\n SELECT People_ID, Name, Age, hometown, distance AS people_distance FROM people_filtered AS people\n)\n\nSELECT sg.Gymnast_ID, sp.People_ID, sg.Total_Points, sp.Name, sp.Age, sg.gymnast_distance, sp.people_distance FROM SimilarGymnasts sg JOIN SimilarPeople sp ON toString(sg.Gymnast_ID) = toString(sp.People_ID) ORDER BY sg.gymnast_distance + sp.people_distance LIMIT 10;" + ], + "integration_level": 7, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: Missing columns: 'hometown' while processing query: 'WITH [0.04807549715042114, -0.00010898063919739798, -0.05222613364458084, -0.0915665253996849, -0.13357429206371307, 0.012184836901724339, 0.02152576483786106, 0.017031297087669373, 0.0027201203629374504, -0.0009171484271064401, -0.01116049662232399, -0.015347154811024666, 0.040731798857450485, 0.027575228363275528, 0.02664358913898468, 0.03220924735069275, 0.04496605321764946, 0.11717129498720169, -0.05381558835506439, 0.0030505177564918995, -0.006587908137589693, -0.06175994127988815, 0.005093744024634361, 0.07150169461965561, -0.08733963221311569, 0.031612567603588104, -0.07026706635951996, -0.027830785140395164, 0.0667235404253006, -0.004302303306758404, -0.06026464328169823, -0.1375996172428131, -0.033368561416864395, 0.000745691591873765, -0.1522139608860016, -0.026007266715168953, -0.02738247811794281, 0.006919089239090681, -0.05549357458949089, 0.020803077146410942, -0.059889551252126694, 0.022139672189950943, 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0.11561962962150574, 0.00716668926179409, -0.1013917624950409, 0.007694261614233255, -0.04037496820092201, 0.0816093161702156, 0.05601140484213829, -0.04075830057263374, -0.0764710009098053, -0.004426292609423399, -0.05917707458138466, -0.09334374219179153, -0.025111041963100433, -0.01578364148736, -0.016710998490452766, -0.03755506873130798, -0.02012716419994831, -0.04557642340660095, -0.07994505763053894, -0.07726556807756424, -0.03989909589290619, -0.013157329522073269, 0.0001984957343665883, -0.014671813696622849, 0.00865217112004757, 0.03590315207839012, 0.044914696365594864, 0.053144387900829315, -0.11899039149284363, -0.06481576710939407, 0.019408324733376503, -1.4571551076869582e-8, 0.05333341658115387, -0.04139295965433121, -0.055062808096408844, -0.05311177670955658, -0.02410244569182396, -0.023308608680963516, 0.08347528427839279, -0.09370166808366776, -0.02711441181600094, 0.09021112322807312, -0.04439719393849373, 0.06449873000383377, 0.15522681176662445, -0.01672697253525257, 0.014677509665489197, -0.05864260345697403, 0.06815987825393677, 0.11350101232528687, -0.008723859675228596, -0.01670255698263645, 0.03495185822248459, 0.024107929319143295, 0.08606602996587753, -0.050504982471466064, 0.04529237374663353, -0.02052086777985096, 0.006985342130064964, 0.04633650928735733, -0.015866044908761978, 0.017626989632844925, -0.02210473269224167, 0.026336077600717545, -0.006011369172483683, -0.09541120380163193, 0.03710266575217247, 0.05010551959276199, -0.09312474727630615, -0.028868157416582108, -0.06431470066308975, -0.1293010413646698, 0.03911085054278374, 0.032860442996025085, -0.09119360148906708, 0.048840150237083435, 0.0740145817399025, -0.06059517338871956, 0.017114343121647835, -0.03714669123291969, 0.031736139208078384, -0.015265488997101784, -0.07726635783910751, -0.05577811971306801, 0.10875263810157776, -0.0337766669690609, 0.0012209477135911584, -0.06789249181747437, 0.03802748769521713, 0.0970655307173729, 0.04622649401426315, -0.06111257150769234, -0.05567445978522301, -0.031154843047261238, -0.003629109123721719, -0.08513399213552475] AS ref_vec_1 SELECT People_ID, Name, Age, hometown, distance AS people_distance FROM people_filtered AS people', required columns: 'People_ID' 'Name' 'Age' 'hometown' 'distance' 'People_ID' 'Name' 'Age' 'hometown' 'distance'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE gymnast (\n `Gymnast_ID` Nullable(Int64),\n `Floor_Exercise_Points` Nullable(Float64),\n `Pommel_Horse_Points` Nullable(Float64),\n `Rings_Points` Nullable(Float64),\n `Vault_Points` Nullable(Float64),\n `Parallel_Bars_Points` Nullable(Float64),\n `Horizontal_Bar_Points` Nullable(Float64),\n `Total_Points` Nullable(Float64),\n `gymnast_description` Nullable(String),\n `gymnast_description_embedding` Array(Float32)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Float64),\n `Height` Nullable(Float64),\n `Hometown` Nullable(String),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);" + }, + { + "db_id": "wine_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A fine red wine from California with excellent flavor and aroma') AS ref_vec_0\n\nSELECT w.Name, w.Price, w.Score, distance(w.wine_description_embedding, ref_vec_0) AS distance\nFROM wine AS w\nJOIN grapes AS g ON toString(w.Grape) = toString(g.Grape)\nJOIN appellations AS a ON toString(w.Appelation) = toString(a.Appelation)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the names, prices, quality scores, and similarity distances for the top 3 wines that match the description of a fine red wine from California with excellent flavor and aroma?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Premium California red wine with outstanding taste and bouquet') AS ref_vec_0\n\nSELECT w.Name, w.Price, w.Score, distance(w.wine_description_embedding, ref_vec_0) AS distance FROM wine AS w JOIN grapes AS g ON toString(w.Grape) = toString(g.Grape) JOIN appellations AS a ON toString(w.Appelation) = toString(a.Appelation)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exquisite red wine from California known for its rich flavor and aroma') AS ref_vec_0\n\nSELECT w.Name, w.Price, w.Score, distance(w.wine_description_embedding, ref_vec_0) AS distance FROM wine AS w JOIN grapes AS g ON toString(w.Grape) = toString(g.Grape) JOIN appellations AS a ON toString(w.Appelation) = toString(a.Appelation)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-tier California red wine with superb taste and fragrance') AS ref_vec_0\n\nSELECT w.Name, w.Price, w.Score, distance(w.wine_description_embedding, ref_vec_0) AS distance FROM wine AS w JOIN grapes AS g ON toString(w.Grape) = toString(g.Grape) JOIN appellations AS a ON toString(w.Appelation) = toString(a.Appelation)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'California red wine with excellent flavor profile and aromatic qualities') AS ref_vec_0\n\nSELECT w.Name, w.Price, w.Score, distance(w.wine_description_embedding, ref_vec_0) AS distance FROM wine AS w JOIN grapes AS g ON toString(w.Grape) = toString(g.Grape) JOIN appellations AS a ON toString(w.Appelation) = toString(a.Appelation)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Fine red wine from California featuring remarkable taste and scent') AS ref_vec_0\n\nSELECT w.Name, w.Price, w.Score, distance(w.wine_description_embedding, ref_vec_0) AS distance FROM wine AS w JOIN grapes AS g ON toString(w.Grape) = toString(g.Grape) JOIN appellations AS a ON toString(w.Appelation) = toString(a.Appelation)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'wine_description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE appellations (\n `No` Nullable(Int64),\n `Appelation` Nullable(String),\n `County` Nullable(String),\n `State` Nullable(String),\n `Area` Nullable(String),\n `isAVA` Nullable(String),\n `appellations_description` Nullable(String),\n `appellations_description_embedding` Array(Float32)\n);\nCREATE TABLE grapes (\n `ID` Nullable(Int64),\n `Grape` Nullable(String),\n `Color` Nullable(String),\n `grapes_description` Nullable(String),\n `grapes_description_embedding` Array(Float32)\n);\nCREATE TABLE wine (\n `No` Nullable(Int64),\n `Grape` Nullable(String),\n `Winery` Nullable(String),\n `Appelation` Nullable(String),\n `State` Nullable(String),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Price` Nullable(Int64),\n `Score` Nullable(Int64),\n `Cases` Nullable(Int64),\n `Drink` Nullable(String),\n `wine_description` Nullable(String),\n `wine_description_embedding` Array(Float32)\n);" + } +] \ No newline at end of file diff --git a/benchmark/data/results/spider/input_llm.json b/benchmark/data/results/spider/input_llm.json new file mode 100644 index 0000000..b5de54e --- /dev/null +++ b/benchmark/data/results/spider/input_llm.json @@ -0,0 +1,2914 @@ +[ + { + "db_id": "activity_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old student majoring in computer science from New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An outdoor recreational activity involving water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance\n FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance\n FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name\nFROM FilteredStudents fs\nJOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid)\nJOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the 5 students who are just like a 20-year-old majoring in computer science from New York, and tell me their names along with the top 5 water sports activities they do?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A young adult, 20 years old, studying computer science, residing in New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A leisure activity involving water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old computer science student living in New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Water-based recreational sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old from New York studying computer science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Water sports activities for fun') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A New York-based 20-year-old computer science major') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Outdoor water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old computer science undergraduate from New York') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Recreational activities involving water sports') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nActivity_filtered AS (\n SELECT\n *,\n distance(Activity_description_embedding, ref_vec_1) AS distance\n FROM Activity\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredStudents AS (\n SELECT StuID, Fname, LName, distance FROM Student_filtered AS Student\n),\n\nFilteredActivities AS (\n SELECT actid, activity_name, distance FROM Activity_filtered AS Activity\n)\n\nSELECT fs.StuID, fs.Fname, fa.activity_name FROM FilteredStudents fs JOIN Participates_in pi ON toString(fs.StuID) = toString(pi.stuid) JOIN FilteredActivities fa ON toString(pi.actid) = toString(fa.actid);" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Activity (\n `actid` Nullable(Int64),\n `activity_name` Nullable(String),\n `Activity_description` Nullable(String),\n `Activity_description_embedding` Array(Float32)\n);\nCREATE TABLE Activity_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Faculty_Participates_in (\n `FacID` Nullable(Int64),\n `actid` Nullable(Int64)\n);\nCREATE TABLE Faculty_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Participates_in (\n `stuid` Nullable(Int64),\n `actid` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Activity (\n `actid` Nullable(Int64),\n `activity_name` Nullable(String),\n `Activity_description` Nullable(String),\n `Activity_description_embedding` Array(Float32)\n);\nCREATE TABLE Activity_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Activity_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Faculty_Participates_in (\n `FacID` Nullable(Int64),\n `actid` Nullable(Int64)\n);\nCREATE TABLE Faculty_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Faculty_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Participates_in (\n `stuid` Nullable(Int64),\n `actid` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you find me the 5 students who are just like a 20-year-old majoring in computer science from New York, and tell me their names along with the top 5 water sports activities they do?\n\nLet's think step by step!\n" + }, + { + "db_id": "climbing", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'high peak in the Himalayas with difficult climb') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'experienced climber from Nepal who has won many accolades') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT \n Mountain_ID, \n Name, \n Height, \n Range, \n Country,\n distance\n FROM mountain_filtered AS mountain\n)\n\nSELECT \n c.Climber_ID AS Climber_ID\nFROM c_filtered AS c\nJOIN \n FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID)\nORDER BY \n fm.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the top 3 climbers from Nepal who have won many awards and are super experienced? They should be the ones who have climbed the top 5 high peaks in the Himalayas known for tough climbs. Could you also sort the results by how closely the mountains fit the description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'top Himalayan peaks with challenging climbs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Nepalese climbers with numerous awards and extensive experience') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n WHERE climber_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Nepalese climbers with numerous awards AND extensive experience')\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Himalayan summits known for difficult ascents') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'acclaimed climbers from Nepal with significant achievements') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'high-altitude peaks in the Himalayas requiring expert climbing skills') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'top Nepalese climbers with a history of winning awards') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'notable Himalayan peaks with strenuous climbing routes') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Nepal climbers renowned for their climbing prowess and accolades') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n WHERE climber_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Nepal climbers renowned for their climbing prowess AND accolades')\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Himalayan mountains famous for their difficult climbs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'skilled climbers from Nepal with numerous awards') AS ref_vec_1,\n\nmountain_filtered AS (\n SELECT\n *,\n distance(mountain_description_embedding, ref_vec_0) AS distance\n FROM mountain\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(climber_description_embedding, ref_vec_1) AS distance\n FROM climber\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredMountains AS (\n SELECT Mountain_ID, Name, Height, Range, Country, distance FROM mountain_filtered AS mountain\n)\n\nSELECT c.Climber_ID FROM c_filtered AS c JOIN FilteredMountains fm ON toString(c.Mountain_ID) = toString(fm.Mountain_ID) ORDER BY fm.distance;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE climber (\n `Climber_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Time` Nullable(String),\n `Points` Nullable(Float64),\n `Mountain_ID` Nullable(Int64),\n `climber_description` Nullable(String),\n `climber_description_embedding` Array(Float32)\n);\nCREATE TABLE mountain (\n `Mountain_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Height` Nullable(Float64),\n `Prominence` Nullable(Float64),\n `Range` Nullable(String),\n `Country` Nullable(String),\n `mountain_description` Nullable(String),\n `mountain_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE climber (\n `Climber_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Time` Nullable(String),\n `Points` Nullable(Float64),\n `Mountain_ID` Nullable(Int64),\n `climber_description` Nullable(String),\n `climber_description_embedding` Array(Float32)\n);\nCREATE TABLE mountain (\n `Mountain_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Height` Nullable(Float64),\n `Prominence` Nullable(Float64),\n `Range` Nullable(String),\n `Country` Nullable(String),\n `mountain_description` Nullable(String),\n `mountain_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you find me the top 3 climbers from Nepal who have won many awards and are super experienced? They should be the ones who have climbed the top 5 high peaks in the Himalayas known for tough climbs. Could you also sort the results by how closely the mountains fit the description?\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_orders", + "sql": "SELECT order_id, order_status\nFROM Orders;", + "sql_result_column_count": 2, + "sql_result_rows_count": 15, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the order IDs and their current statuses from the Orders table?", + "external_knowledge": "", + "sql_candidate": [ + "SELECT order_id, order_status\nFROM Orders;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_name` Nullable(String),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Invoices (\n `invoice_number` Nullable(Int64),\n `invoice_date` Nullable(Date),\n `invoice_details` Nullable(String),\n `Invoices_description` Nullable(String)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Nullable(Int64),\n `product_id` Int64,\n `order_id` Int64,\n `order_item_status` String,\n `order_item_details` Nullable(String)\n);\nCREATE TABLE Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status` String,\n `date_order_placed` Date,\n `order_details` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Products_description` Nullable(String)\n);\nCREATE TABLE Shipment_Items (\n `shipment_id` Int64,\n `order_item_id` Int64\n);\nCREATE TABLE Shipments (\n `shipment_id` Nullable(Int64),\n `order_id` Int64,\n `invoice_number` Int64,\n `shipment_tracking_number` Nullable(String),\n `shipment_date` Nullable(Date),\n `other_shipment_details` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_name` Nullable(String),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Invoices (\n `invoice_number` Nullable(Int64),\n `invoice_date` Nullable(Date),\n `invoice_details` Nullable(String),\n `Invoices_description` Nullable(String)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Nullable(Int64),\n `product_id` Int64,\n `order_id` Int64,\n `order_item_status` String,\n `order_item_details` Nullable(String)\n);\nCREATE TABLE Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status` String,\n `date_order_placed` Date,\n `order_details` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Products_description` Nullable(String)\n);\nCREATE TABLE Shipment_Items (\n `shipment_id` Int64,\n `order_item_id` Int64\n);\nCREATE TABLE Shipments (\n `shipment_id` Nullable(Int64),\n `order_id` Int64,\n `invoice_number` Int64,\n `shipment_tracking_number` Nullable(String),\n `shipment_date` Nullable(Date),\n `other_shipment_details` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the order IDs and their current statuses from the Orders table?\n\nLet's think step by step!\n" + }, + { + "db_id": "company_office", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'under construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance\nFROM buildings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "What is the ID of the building that is most closely associated with being \"under construction\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'currently being built') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'in progress construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'ongoing construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'construction phase') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'actively under construction') AS ref_vec_0\n\nSELECT id, distance(buildings.Status_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat is the ID of the building that is most closely associated with being \"under construction\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "entertainment_awards", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Annual festival with large audience') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance\nFROM festival_detail\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the name of a festival that's known for being a big annual event with a huge crowd?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Major annual event with a massive crowd') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Large-scale yearly festival with many attendees') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Popular annual festival attracting huge crowds') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Annual celebration known for large gatherings') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Big yearly festival with a significant audience') AS ref_vec_0\n\nSELECT Festival_Name, distance(festival_detail.festival_detail_description_embedding, ref_vec_0) AS distance FROM festival_detail\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artwork (\n `Artwork_ID` Nullable(Int64),\n `Type` Nullable(String),\n `Name` Nullable(String),\n `artwork_description` Nullable(String),\n `artwork_description_embedding` Array(Float32)\n);\nCREATE TABLE festival_detail (\n `Festival_ID` Nullable(Int64),\n `Festival_Name` Nullable(String),\n `Chair_Name` Nullable(String),\n `Location` Nullable(String),\n `Year` Nullable(Int64),\n `Num_of_Audience` Nullable(Int64),\n `festival_detail_description` Nullable(String),\n `festival_detail_description_embedding` Array(Float32)\n);\nCREATE TABLE nomination (\n `Artwork_ID` Nullable(Int64),\n `Festival_ID` Nullable(Int64),\n `Result` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE artwork (\n `Artwork_ID` Nullable(Int64),\n `Type` Nullable(String),\n `Name` Nullable(String),\n `artwork_description` Nullable(String),\n `artwork_description_embedding` Array(Float32)\n);\nCREATE TABLE festival_detail (\n `Festival_ID` Nullable(Int64),\n `Festival_Name` Nullable(String),\n `Chair_Name` Nullable(String),\n `Location` Nullable(String),\n `Year` Nullable(Int64),\n `Num_of_Audience` Nullable(Int64),\n `festival_detail_description` Nullable(String),\n `festival_detail_description_embedding` Array(Float32)\n);\nCREATE TABLE nomination (\n `Artwork_ID` Nullable(Int64),\n `Festival_ID` Nullable(Int64),\n `Result` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the name of a festival that's known for being a big annual event with a huge crowd?\n\nLet's think step by step!\n" + }, + { + "db_id": "company_office", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A skyscraper in a major metropolitan area with over 50 stories and modern architectural design') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance\nFROM buildings\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the five skyscrapers located in major metropolitan areas with over 50 stories and modern architectural design, and provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Skyscrapers in large cities with more than 50 floors and contemporary architecture') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tall buildings in urban centers over 50 stories high with modern design') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-rise structures in metropolitan areas featuring 50+ floors and modern architecture') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Buildings in major cities exceeding 50 stories with contemporary architectural style') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Modern skyscrapers in populous urban regions with more than 50 levels') AS ref_vec_0\n\nSELECT id, name, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the five skyscrapers located in major metropolitan areas with over 50 stories and modern architectural design, and provide their IDs and names.\n\nLet's think step by step!\n" + }, + { + "db_id": "company_office", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A tall skyscraper with modern architecture in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance \nFROM buildings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you identify the building that best represents a tall skyscraper with modern architecture in the city and provide its ID?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The most iconic modern skyscraper in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent high-rise with contemporary design in the urban area') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The tallest modern architectural building in the metropolis') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A leading example of modern skyscraper architecture in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An exemplary tall structure with modern design in the city') AS ref_vec_0\n\nSELECT id, distance(buildings.buildings_description_embedding, ref_vec_0) AS distance FROM buildings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Companies (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Headquarters` Nullable(String),\n `Industry` Nullable(String),\n `Sales_billion` Nullable(Float64),\n `Profits_billion` Nullable(Float64),\n `Assets_billion` Nullable(Float64),\n `Market_Value_billion` Nullable(String),\n `Companies_description` Nullable(String),\n `Companies_description_embedding` Array(Float32)\n);\nCREATE TABLE Office_locations (\n `building_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `move_in_year` Nullable(Int64)\n);\nCREATE TABLE buildings (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `City` Nullable(String),\n `Height` Nullable(Int64),\n `Stories` Nullable(Int64),\n `Status` Nullable(String),\n `buildings_description` Nullable(String),\n `Status_embedding` Array(Float32),\n `buildings_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you identify the building that best represents a tall skyscraper with modern architecture in the city and provide its ID?\n\nLet's think step by step!\n" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Rock and roll album with classic hits') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance \nFROM Album\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Which album is the closest match to a rock and roll album with classic hits?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'classic rock and roll hits album') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'album featuring classic rock and roll tracks') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'rock and roll album with timeless classics') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'album with iconic rock and roll songs') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'collection of classic rock and roll music') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhich album is the closest match to a rock and roll album with classic hits?\n\nLet's think step by step!\n" + }, + { + "db_id": "journal_committee", + "sql": "WITH RankedJournals AS (\n SELECT \n j.Journal_ID AS Journal_ID, \n j.Sales AS Sales, \n RANK() OVER (ORDER BY j.Sales DESC) AS SalesRank\n FROM \n journal j\n),\nTopEditors AS (\n SELECT \n jc.Editor_ID AS Editor_ID,\n e.Name AS Name,\n COUNT(DISTINCT rj.Journal_ID) AS NumberOfTopJournals\n FROM \n RankedJournals rj\n JOIN \n journal_committee jc ON toString(rj.Journal_ID) = toString(jc.Journal_ID)\n JOIN \n editor e ON toString(jc.Editor_ID) = toString(e.Editor_ID)\n WHERE \n rj.SalesRank <= 5 \n GROUP BY \n jc.Editor_ID, e.Name\n ORDER BY \n NumberOfTopJournals DESC\n)\nSELECT \n te.Name AS Name\nFROM \n TopEditors te\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the editor who is associated with the highest number of top 5 best-selling journals and provide me with their name?", + "external_knowledge": "", + "sql_candidate": [ + "WITH RankedJournals AS (\n SELECT \n j.Journal_ID AS Journal_ID, \n j.Sales AS Sales, \n RANK() OVER (ORDER BY j.Sales DESC) AS SalesRank\n FROM \n journal j\n),\nTopEditors AS (\n SELECT \n jc.Editor_ID AS Editor_ID,\n e.Name AS Name,\n COUNT(DISTINCT rj.Journal_ID) AS NumberOfTopJournals\n FROM \n RankedJournals rj\n JOIN \n journal_committee jc ON toString(rj.Journal_ID) = toString(jc.Journal_ID)\n JOIN \n editor e ON toString(jc.Editor_ID) = toString(e.Editor_ID)\n WHERE \n rj.SalesRank <= 5 \n GROUP BY \n jc.Editor_ID, e.Name\n ORDER BY \n NumberOfTopJournals DESC\n)\nSELECT \n te.Name AS Name\nFROM \n TopEditors te\nLIMIT 1;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE editor (\n `Editor_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Float64),\n `editor_description` Nullable(String)\n);\nCREATE TABLE journal (\n `Journal_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Theme` Nullable(String),\n `Sales` Nullable(Int64),\n `journal_description` Nullable(String)\n);\nCREATE TABLE journal_committee (\n `Editor_ID` Nullable(Int64),\n `Journal_ID` Nullable(Int64),\n `Work_Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE editor (\n `Editor_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Float64),\n `editor_description` Nullable(String)\n);\nCREATE TABLE journal (\n `Journal_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Theme` Nullable(String),\n `Sales` Nullable(Int64),\n `journal_description` Nullable(String)\n);\nCREATE TABLE journal_committee (\n `Editor_ID` Nullable(Int64),\n `Journal_ID` Nullable(Int64),\n `Work_Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the editor who is associated with the highest number of top 5 best-selling journals and provide me with their name?\n\nLet's think step by step!\n" + }, + { + "db_id": "insurance_fnol", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Customer referred to as America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance\nFROM Customers c\nJOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the Customer_ID of the customer most closely matching the description of \"America Jaskolski with ID 194\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Customer identified as America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Find customer called America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Locate customer known as America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for customer America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Customer named America Jaskolski with ID 194') AS ref_vec_0\n\nSELECT c.Customer_ID, distance(c.Customers_description_embedding, ref_vec_0) AS distance FROM Customers c JOIN Customers_Policies cp ON toString(c.Customer_ID) = toString(cp.Customer_ID)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Available_Policies (\n `Policy_ID` Nullable(Int64),\n `policy_type_code` Nullable(String),\n `Customer_Phone` Nullable(String),\n `Available_Policies_description` Nullable(String),\n `Available_Policies_description_embedding` Array(Float32)\n);\nCREATE TABLE Available_Policies_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Claims (\n `Claim_ID` Int64,\n `FNOL_ID` Int64,\n `Effective_Date` Nullable(Date)\n);\nCREATE TABLE Customers (\n `Customer_ID` Nullable(Int64),\n `Customer_name` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers_Policies (\n `Customer_ID` Int64,\n `Policy_ID` Int64,\n `Date_Opened` Nullable(Date),\n `Date_Closed` Nullable(Date)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE First_Notification_of_Loss (\n `FNOL_ID` Int64,\n `Customer_ID` Int64,\n `Policy_ID` Int64,\n `Service_ID` Int64\n);\nCREATE TABLE Services (\n `Service_ID` Nullable(Int64),\n `Service_name` Nullable(String),\n `Services_description` Nullable(String),\n `Services_description_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Settlements (\n `Settlement_ID` Int64,\n `Claim_ID` Nullable(Int64),\n `Effective_Date` Nullable(Date),\n `Settlement_Amount` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Available_Policies (\n `Policy_ID` Nullable(Int64),\n `policy_type_code` Nullable(String),\n `Customer_Phone` Nullable(String),\n `Available_Policies_description` Nullable(String),\n `Available_Policies_description_embedding` Array(Float32)\n);\nCREATE TABLE Available_Policies_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Available_Policies_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Claims (\n `Claim_ID` Int64,\n `FNOL_ID` Int64,\n `Effective_Date` Nullable(Date)\n);\nCREATE TABLE Customers (\n `Customer_ID` Nullable(Int64),\n `Customer_name` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers_Policies (\n `Customer_ID` Int64,\n `Policy_ID` Int64,\n `Date_Opened` Nullable(Date),\n `Date_Closed` Nullable(Date)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE First_Notification_of_Loss (\n `FNOL_ID` Int64,\n `Customer_ID` Int64,\n `Policy_ID` Int64,\n `Service_ID` Int64\n);\nCREATE TABLE Services (\n `Service_ID` Nullable(Int64),\n `Service_name` Nullable(String),\n `Services_description` Nullable(String),\n `Services_description_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Settlements (\n `Settlement_ID` Int64,\n `Claim_ID` Nullable(Int64),\n `Effective_Date` Nullable(Date),\n `Settlement_Amount` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the Customer_ID of the customer most closely matching the description of \"America Jaskolski with ID 194\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renewable energy project focused on solar and wind power') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance \nFROM Projects\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you find me the top project that's all about renewable energy with a focus on solar and wind power? I just need the project ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A project on renewable energy with emphasis on solar and wind') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top renewable energy project focusing on solar and wind') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading project in renewable energy, specializing in solar and wind power') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renewable energy initiative centered around solar and wind energy') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Major renewable energy project with solar and wind focus') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you find me the top project that's all about renewable energy with a focus on solar and wind power? I just need the project ID.\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of renewable energy initiatives') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance\nFROM Projects\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the project that best matches the exploration of renewable energy initiatives and share its ID with me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Investigation into renewable energy projects') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research on sustainable energy solutions') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Study of renewable energy initiatives') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Analysis of green energy projects') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exploration of sustainable energy initiatives') AS ref_vec_0\n\nSELECT project_id, distance(Projects.project_details_embedding, ref_vec_0) AS distance FROM Projects\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the project that best matches the exploration of renewable energy initiatives and share its ID with me?\n\nLet's think step by step!\n" + }, + { + "db_id": "store_product", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'High-resolution scanner with USB connectivity') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance\nFROM product\nORDER BY distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the IDs and names of the 2 products that best fit the description of a \"High-resolution scanner with USB connectivity\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'USB-enabled high-resolution scanner') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-res scanner with USB port') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Scanner with USB connection and high resolution') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-definition scanner featuring USB connectivity') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced scanner with USB interface and high resolution') AS ref_vec_0\n\nSELECT product_id, product, distance(product.product_description_embedding, ref_vec_0) AS distance FROM product\nORDER BY distance\nLIMIT 2;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE district (\n `District_ID` Nullable(Int64),\n `District_name` Nullable(String),\n `Headquartered_City` Nullable(String),\n `City_Population` Nullable(Float64),\n `City_Area` Nullable(Float64),\n `district_description` Nullable(String),\n `district_description_embedding` Array(Float32)\n);\nCREATE TABLE product (\n `product_id` Nullable(Int64),\n `product` Nullable(String),\n `dimensions` Nullable(String),\n `dpi` Nullable(Float64),\n `pages_per_minute_color` Nullable(Float64),\n `max_page_size` Nullable(String),\n `interface` Nullable(String),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE store (\n `Store_ID` Nullable(Int64),\n `Store_Name` Nullable(String),\n `Type` Nullable(String),\n `Area_size` Nullable(Float64),\n `Number_of_product_category` Nullable(Float64),\n `Ranking` Nullable(Int64),\n `store_description` Nullable(String),\n `store_description_embedding` Array(Float32)\n);\nCREATE TABLE store_district (\n `Store_ID` Nullable(Int64),\n `District_ID` Nullable(Int64)\n);\nCREATE TABLE store_product (\n `Store_ID` Nullable(Int64),\n `Product_ID` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE district (\n `District_ID` Nullable(Int64),\n `District_name` Nullable(String),\n `Headquartered_City` Nullable(String),\n `City_Population` Nullable(Float64),\n `City_Area` Nullable(Float64),\n `district_description` Nullable(String),\n `district_description_embedding` Array(Float32)\n);\nCREATE TABLE product (\n `product_id` Nullable(Int64),\n `product` Nullable(String),\n `dimensions` Nullable(String),\n `dpi` Nullable(Float64),\n `pages_per_minute_color` Nullable(Float64),\n `max_page_size` Nullable(String),\n `interface` Nullable(String),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE store (\n `Store_ID` Nullable(Int64),\n `Store_Name` Nullable(String),\n `Type` Nullable(String),\n `Area_size` Nullable(Float64),\n `Number_of_product_category` Nullable(Float64),\n `Ranking` Nullable(Int64),\n `store_description` Nullable(String),\n `store_description_embedding` Array(Float32)\n);\nCREATE TABLE store_district (\n `Store_ID` Nullable(Int64),\n `District_ID` Nullable(Int64)\n);\nCREATE TABLE store_product (\n `Store_ID` Nullable(Int64),\n `Product_ID` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the IDs and names of the 2 products that best fit the description of a \"High-resolution scanner with USB connectivity\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'player with excellent skills and no yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance\nFROM Player p\nJOIN Tryout t ON toString(p.pID) = toString(t.pID)\nWHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the names of the top 5 players who are highly skilled and have no yellow cards and were accepted in a tryout?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'highly skilled player without yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top player with no yellow cards and great skills') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'elite player, skillful and no yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'player with high skill level and zero yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'exceptionally skilled player without any yellow cards') AS ref_vec_0\n\nSELECT p.pName, distance(p.Player_description_embedding, ref_vec_0) AS distance FROM Player p JOIN Tryout t ON toString(p.pID) = toString(t.pID) WHERE t.decision = 'yes'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE College (\n `cName` Nullable(String),\n `state` Nullable(String),\n `enr` Nullable(Float64),\n `College_description` Nullable(String),\n `College_description_embedding` Array(Float32)\n);\nCREATE TABLE College_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Player (\n `pID` Nullable(Float64),\n `pName` Nullable(String),\n `yCard` Nullable(String),\n `HS` Nullable(Float64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Tryout (\n `pID` Nullable(Decimal(38, 6)),\n `cName` Nullable(String),\n `pPos` Nullable(String),\n `decision` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE College (\n `cName` Nullable(String),\n `state` Nullable(String),\n `enr` Nullable(Float64),\n `College_description` Nullable(String),\n `College_description_embedding` Array(Float32)\n);\nCREATE TABLE College_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE College_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Player (\n `pID` Nullable(Float64),\n `pName` Nullable(String),\n `yCard` Nullable(String),\n `HS` Nullable(Float64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Player_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Tryout (\n `pID` Nullable(Decimal(38, 6)),\n `cName` Nullable(String),\n `pPos` Nullable(String),\n `decision` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the names of the top 5 players who are highly skilled and have no yellow cards and were accepted in a tryout?\n\nLet's think step by step!\n" + }, + { + "db_id": "local_govt_mdm", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A company specializing in scientific journals and books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance\nFROM Customer_Master_Index cmi\nJOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 9, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Can you help me out by finding the top 5 customer IDs and their descriptions for companies that specialize in scientific journals and books? I'd also love to know how close each match is. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Organizations focused on publishing scientific literature and academic books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Businesses that produce scientific journals and educational books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Enterprises involved in the distribution of scientific publications and textbooks.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Companies that specialize in the publication of scientific and academic resources.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Firms dedicated to the creation of scientific articles and scholarly books.') AS ref_vec_0\n\nSELECT cmi.master_customer_id, cmi.Customer_Master_Index_description, distance(cmi.Customer_Master_Index_description_embedding, ref_vec_0) AS distance FROM Customer_Master_Index cmi JOIN CMI_Cross_References cr ON toString(cmi.master_customer_id) = toString(cr.master_customer_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Benefits_Overpayments (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Business_Rates (\n `business_rates_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE CMI_Cross_References (\n `cmi_cross_ref_id` Int64,\n `master_customer_id` Int64,\n `source_system_code` String\n);\nCREATE TABLE Council_Tax (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Customer_Master_Index (\n `master_customer_id` Nullable(Int64),\n `cmi_details` Nullable(String),\n `Customer_Master_Index_description` Nullable(String),\n `cmi_details_embedding` Array(Float32),\n `Customer_Master_Index_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Electoral_Register (\n `electoral_register_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Parking_Fines (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Rent_Arrears (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Benefits_Overpayments (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Business_Rates (\n `business_rates_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE CMI_Cross_References (\n `cmi_cross_ref_id` Int64,\n `master_customer_id` Int64,\n `source_system_code` String\n);\nCREATE TABLE Council_Tax (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Customer_Master_Index (\n `master_customer_id` Nullable(Int64),\n `cmi_details` Nullable(String),\n `Customer_Master_Index_description` Nullable(String),\n `cmi_details_embedding` Array(Float32),\n `Customer_Master_Index_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Customer_Master_Index_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Electoral_Register (\n `electoral_register_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Parking_Fines (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\nCREATE TABLE Rent_Arrears (\n `council_tax_id` Int64,\n `cmi_cross_ref_id` Int64\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you help me out by finding the top 5 customer IDs and their descriptions for companies that specialize in scientific journals and books? I'd also love to know how close each match is. Thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "mountain_photos", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A towering peak in the Himalayas known for its rugged terrain and breathtaking views.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance\nFROM mountain\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Identify the mountain that best matches the description of a towering Himalayan peak with rugged terrain and breathtaking views. Provide its ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A majestic Himalayan mountain with steep slopes and stunning vistas.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent peak in the Himalayas featuring rugged landscapes and scenic views.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A towering Himalayan summit known for its challenging terrain and spectacular scenery.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A high-altitude Himalayan mountain with rough terrain and breathtaking panoramas.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A striking Himalayan peak characterized by its rugged environment and awe-inspiring views.') AS ref_vec_0\n\nSELECT id, distance(mountain.mountain_description_embedding, ref_vec_0) AS distance FROM mountain\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE camera_lens (\n `id` Nullable(Int64),\n `brand` Nullable(String),\n `name` Nullable(String),\n `focal_length_mm` Nullable(Float64),\n `max_aperture` Nullable(Float64),\n `camera_lens_description` Nullable(String),\n `camera_lens_description_embedding` Array(Float32)\n);\nCREATE TABLE mountain (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Height` Nullable(Float64),\n `Prominence` Nullable(Float64),\n `Range` Nullable(String),\n `Country` Nullable(String),\n `mountain_description` Nullable(String),\n `mountain_description_embedding` Array(Float32)\n);\nCREATE TABLE photos (\n `id` Nullable(Int64),\n `camera_lens_id` Nullable(Int64),\n `mountain_id` Nullable(Int64),\n `color` Nullable(String),\n `name` Nullable(String),\n `photos_description` Nullable(String),\n `photos_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE camera_lens (\n `id` Nullable(Int64),\n `brand` Nullable(String),\n `name` Nullable(String),\n `focal_length_mm` Nullable(Float64),\n `max_aperture` Nullable(Float64),\n `camera_lens_description` Nullable(String),\n `camera_lens_description_embedding` Array(Float32)\n);\nCREATE TABLE mountain (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Height` Nullable(Float64),\n `Prominence` Nullable(Float64),\n `Range` Nullable(String),\n `Country` Nullable(String),\n `mountain_description` Nullable(String),\n `mountain_description_embedding` Array(Float32)\n);\nCREATE TABLE photos (\n `id` Nullable(Int64),\n `camera_lens_id` Nullable(Int64),\n `mountain_id` Nullable(Int64),\n `color` Nullable(String),\n `name` Nullable(String),\n `photos_description` Nullable(String),\n `photos_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the mountain that best matches the description of a towering Himalayan peak with rugged terrain and breathtaking views. Provide its ID.\n\nLet's think step by step!\n" + }, + { + "db_id": "tvshow", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling episode with unexpected plot twists and high ratings') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance\n FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name\nFROM TV_Channel t\nJOIN SeriesKNN s ON toString(t.series_name) = toString(s.id)\nORDER BY s.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "What are the names of the top five TV series that align with a thrilling narrative filled with unexpected plot twists and high ratings?", + "external_knowledge": "In vector search operations, the \"MATCH\" operator is used for approximate nearest neighbor search to find vectors that are closest in terms of Euclidean distance. The \"k=5\" in the query specifies that the top 5 items are to be retrieved. In this context, embeddings are numerical representations of text data that capture semantic meaning. The series descriptions are compared to the embedding of a specified phrase to find the most semantically similar entries, with the smallest distance indicating the highest similarity.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exhilarating series with surprising plot developments and high viewer ratings') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A suspenseful show with unpredictable storylines and excellent ratings') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-rated thrilling series with unexpected twists and turns') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-rated TV series with thrilling narratives and surprise plot changes') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A captivating series with high ratings and unexpected plot twists') AS ref_vec_0,\n\nSeriesKNN AS (\n SELECT id, distance(TV_series.TV_series_description_embedding, ref_vec_0) AS distance FROM TV_series\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT t.series_name FROM TV_Channel t JOIN SeriesKNN s ON toString(t.series_name) = toString(s.id) ORDER BY s.distance;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Cartoon (\n `id` Nullable(Float64),\n `Title` Nullable(String),\n `Directed_by` Nullable(String),\n `Written_by` Nullable(String),\n `Original_air_date` Nullable(String),\n `Production_code` Nullable(Float64),\n `Channel` Nullable(String),\n `Cartoon_description` Nullable(String),\n `Cartoon_description_embedding` Array(Float32)\n);\nCREATE TABLE TV_Channel (\n `id` Nullable(String),\n `series_name` Nullable(String),\n `Country` Nullable(String),\n `Language` Nullable(String),\n `Content` Nullable(String),\n `Pixel_aspect_ratio_PAR` Nullable(String),\n `Hight_definition_TV` Nullable(String),\n `Pay_per_view_PPV` Nullable(String),\n `Package_Option` Nullable(String),\n `TV_Channel_description` Nullable(String),\n `TV_Channel_description_embedding` Array(Float32)\n);\nCREATE TABLE TV_series (\n `id` Nullable(Float64),\n `Episode` Nullable(String),\n `Air_Date` Nullable(String),\n `Rating` Nullable(String),\n `Share` Nullable(Float64),\n `fld_18_49_Rating_Share` Nullable(String),\n `Viewers_m` Nullable(String),\n `Weekly_Rank` Nullable(Float64),\n `Channel` Nullable(String),\n `TV_series_description` Nullable(String),\n `TV_series_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Cartoon (\n `id` Nullable(Float64),\n `Title` Nullable(String),\n `Directed_by` Nullable(String),\n `Written_by` Nullable(String),\n `Original_air_date` Nullable(String),\n `Production_code` Nullable(Float64),\n `Channel` Nullable(String),\n `Cartoon_description` Nullable(String),\n `Cartoon_description_embedding` Array(Float32)\n);\nCREATE TABLE TV_Channel (\n `id` Nullable(String),\n `series_name` Nullable(String),\n `Country` Nullable(String),\n `Language` Nullable(String),\n `Content` Nullable(String),\n `Pixel_aspect_ratio_PAR` Nullable(String),\n `Hight_definition_TV` Nullable(String),\n `Pay_per_view_PPV` Nullable(String),\n `Package_Option` Nullable(String),\n `TV_Channel_description` Nullable(String),\n `TV_Channel_description_embedding` Array(Float32)\n);\nCREATE TABLE TV_series (\n `id` Nullable(Float64),\n `Episode` Nullable(String),\n `Air_Date` Nullable(String),\n `Rating` Nullable(String),\n `Share` Nullable(Float64),\n `fld_18_49_Rating_Share` Nullable(String),\n `Viewers_m` Nullable(String),\n `Weekly_Rank` Nullable(Float64),\n `Channel` Nullable(String),\n `TV_series_description` Nullable(String),\n `TV_series_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn vector search operations, the \"MATCH\" operator is used for approximate nearest neighbor search to find vectors that are closest in terms of Euclidean distance. The \"k=5\" in the query specifies that the top 5 items are to be retrieved. In this context, embeddings are numerical representations of text data that capture semantic meaning. The series descriptions are compared to the embedding of a specified phrase to find the most semantically similar entries, with the smallest distance indicating the highest similarity.\nWhat are the names of the top five TV series that align with a thrilling narrative filled with unexpected plot twists and high ratings?\n\nLet's think step by step!\n" + }, + { + "db_id": "local_govt_and_lot", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Satisfied services for residents') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance\nFROM Residents_Services rs\nJOIN Properties p ON toString(rs.property_id) = toString(p.property_id)\nWHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top 5 services provided to residents that are considered satisfactory, specifically for properties located in Springfield.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top satisfactory services for Springfield residents') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Services rated satisfactory by Springfield residents') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Springfield properties with satisfactory services') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly rated services for Springfield properties') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Springfield resident services deemed satisfactory') AS ref_vec_0\n\nSELECT rs.service_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Properties p ON toString(rs.property_id) = toString(p.property_id) WHERE p.property_address LIKE '%Springfield%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Customer_Event_Notes (\n `Customer_Event_Note_ID` Int64,\n `Customer_Event_ID` Int64,\n `service_type_code` String,\n `resident_id` Int64,\n `property_id` Int64,\n `date_moved_in` Date,\n `Customer_Event_Notes_description` Nullable(String)\n);\nCREATE TABLE Customer_Events (\n `Customer_Event_ID` Int64,\n `customer_id` Nullable(Int64),\n `date_moved_in` Nullable(Date),\n `property_id` Nullable(Int64),\n `resident_id` Nullable(Int64),\n `thing_id` Int64\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String),\n `customer_details_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `parent_organization_id` Nullable(Int64),\n `organization_details` Nullable(String),\n `Organizations_description` Nullable(String),\n `organization_details_embedding` Array(Float32)\n);\nCREATE TABLE Organizations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` Nullable(String),\n `property_address` Nullable(String),\n `other_details` Nullable(String),\n `Properties_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Properties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents (\n `resident_id` Nullable(Int64),\n `property_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `date_moved_out` Nullable(String),\n `other_details` Nullable(String),\n `Residents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services (\n `resident_id` Nullable(Int64),\n `service_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `property_id` Nullable(Int64),\n `date_requested` Nullable(String),\n `date_provided` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Services_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Things (\n `thing_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `Type_of_Thing_Code` Nullable(String),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Things_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Things_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Timed_Locations_of_Things (\n `thing_id` Int64,\n `Date_and_Time` Date,\n `Location_Code` String\n);\nCREATE TABLE Timed_Status_of_Things (\n `thing_id` Int64,\n `Date_and_Date` Date,\n `Status_of_Thing_Code` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Customer_Event_Notes (\n `Customer_Event_Note_ID` Int64,\n `Customer_Event_ID` Int64,\n `service_type_code` String,\n `resident_id` Int64,\n `property_id` Int64,\n `date_moved_in` Date,\n `Customer_Event_Notes_description` Nullable(String)\n);\nCREATE TABLE Customer_Events (\n `Customer_Event_ID` Int64,\n `customer_id` Nullable(Int64),\n `date_moved_in` Nullable(Date),\n `property_id` Nullable(Int64),\n `resident_id` Nullable(Int64),\n `thing_id` Int64\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String),\n `customer_details_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `parent_organization_id` Nullable(Int64),\n `organization_details` Nullable(String),\n `Organizations_description` Nullable(String),\n `organization_details_embedding` Array(Float32)\n);\nCREATE TABLE Organizations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` Nullable(String),\n `property_address` Nullable(String),\n `other_details` Nullable(String),\n `Properties_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Properties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents (\n `resident_id` Nullable(Int64),\n `property_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `date_moved_out` Nullable(String),\n `other_details` Nullable(String),\n `Residents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services (\n `resident_id` Nullable(Int64),\n `service_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `property_id` Nullable(Int64),\n `date_requested` Nullable(String),\n `date_provided` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Services_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Things (\n `thing_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `Type_of_Thing_Code` Nullable(String),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Things_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Things_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Timed_Locations_of_Things (\n `thing_id` Int64,\n `Date_and_Time` Date,\n `Location_Code` String\n);\nCREATE TABLE Timed_Status_of_Things (\n `thing_id` Int64,\n `Date_and_Date` Date,\n `Status_of_Thing_Code` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 5 services provided to residents that are considered satisfactory, specifically for properties located in Springfield.\n\nLet's think step by step!\n" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A classic rock album with iconic hits') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance\nFROM Album\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need to identify the album that best represents a classic rock album with iconic hits. Could you provide me with the AlbumId for this?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic collection of classic rock hits') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The quintessential classic rock album with famous tracks') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary album featuring classic rock anthems') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A definitive classic rock album known for its hit songs') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A celebrated rock album with classic chart-toppers') AS ref_vec_0\n\nSELECT AlbumId, distance(Album.Album_description_embedding, ref_vec_0) AS distance FROM Album\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI need to identify the album that best represents a classic rock album with iconic hits. Could you provide me with the AlbumId for this?\n\nLet's think step by step!\n" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high-energy rock track with powerful guitar riffs and dynamic vocals.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance\nFROM Track\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Identify the top 3 tracks with high-energy rock themes, characterized by powerful guitar riffs and dynamic vocals.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Energetic rock music featuring strong guitar riffs and dynamic singing.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rock tracks with intense guitar solos and powerful vocal performances.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-energy rock songs with prominent guitar and vibrant vocals.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dynamic rock tracks characterized by bold guitar riffs and energetic vocals.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rock music with powerful guitar riffs and lively vocal dynamics.') AS ref_vec_0\n\nSELECT TrackId, Name, distance(Track.Track_description_embedding, ref_vec_0) AS distance FROM Track\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 3 tracks with high-energy rock themes, characterized by powerful guitar riffs and dynamic vocals.\n\nLet's think step by step!\n" + }, + { + "db_id": "pets_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John, a 20-year-old male majoring in Computer Science, advised by 1234, from New York.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance \nFROM Student\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Please find the student ID and similarity score for the student whose profile most closely matches the description: \"John, a 20-year-old male majoring in Computer Science, advised by 1234, from New York.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the student ID and similarity score for a male student, 20 years old, studying Computer Science, advisor ID 1234, from New York.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '20-year-old Computer Science major, male, advised by 1234, located in New York, find student ID and score.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify a student ID and score for a 20-year-old male in Computer Science with advisor 1234, from New York.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for a student, 20-year-old male in Computer Science, advised by 1234, based in New York, and return ID and similarity.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Locate the student ID and similarity for a New York-based, 20-year-old male studying Computer Science, advised by 1234.') AS ref_vec_0\n\nSELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Has_Pet (\n `StuID` Nullable(Int64),\n `PetID` Nullable(Int64)\n);\nCREATE TABLE Pets (\n `PetID` Nullable(Int64),\n `PetType` Nullable(String),\n `pet_age` Nullable(Int64),\n `weight` Nullable(Float64),\n `Pets_description` Nullable(String),\n `Pets_description_embedding` Array(Float32)\n);\nCREATE TABLE Pets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Has_Pet (\n `StuID` Nullable(Int64),\n `PetID` Nullable(Int64)\n);\nCREATE TABLE Pets (\n `PetID` Nullable(Int64),\n `PetType` Nullable(String),\n `pet_age` Nullable(Int64),\n `weight` Nullable(Float64),\n `Pets_description` Nullable(String),\n `Pets_description_embedding` Array(Float32)\n);\nCREATE TABLE Pets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Pets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease find the student ID and similarity score for the student whose profile most closely matches the description: \"John, a 20-year-old male majoring in Computer Science, advised by 1234, from New York.\"\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Research funding document for innovative projects') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance\nFROM Documents d\nJOIN Grants g ON toString(d.grant_id) = toString(g.grant_id)\nWHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please identify the three most significant documents related to \"Research funding document for innovative projects\" that are associated with grants exceeding $50,000? I need their document IDs and type codes!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Documents on funding for innovative research projects') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative project research funding documents') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Funding documents for research on innovation') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Research funding documents for innovative projects') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Documents related to funding for innovative research') AS ref_vec_0\n\nSELECT d.document_id, d.document_type_code, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN Grants g ON toString(d.grant_id) = toString(g.grant_id) WHERE g.grant_amount > 50000\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please identify the three most significant documents related to \"Research funding document for innovative projects\" that are associated with grants exceeding $50,000? I need their document IDs and type codes!\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_grants_for_research", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Important document regarding funding over $50,000') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount\n FROM Grants\n WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance\nFROM Documents d\nJOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "What are the document IDs for the top 5 documents associated with grants over $50,000, that are most relevant to important funding documents?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top funding documents related to grants exceeding $50,000') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Significant funding documentation for large grants') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key documents on substantial funding grants') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Relevant documents for major funding grants over $50,000') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Important documents linked to high-value grants') AS ref_vec_0,\n\nFilteredGrants AS (\n SELECT grant_id, organisation_id, grant_amount FROM Grants WHERE grant_amount > 50000\n)\n\nSELECT d.document_id, distance(d.other_details_embedding, ref_vec_0) AS distance FROM Documents d JOIN FilteredGrants fg ON toString(d.grant_id) = toString(fg.grant_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Document_Types (\n `document_type_code` Nullable(String),\n `document_description` Nullable(String),\n `document_description_embedding` Array(Float32)\n);\nCREATE TABLE Documents (\n `document_id` Nullable(Int64),\n `document_type_code` Nullable(String),\n `grant_id` Nullable(Int64),\n `sent_date` Nullable(String),\n `response_received_date` Nullable(String),\n `other_details` Nullable(String),\n `Documents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Grants (\n `grant_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `grant_amount` Nullable(Float64),\n `grant_start_date` Nullable(String),\n `grant_end_date` Nullable(String),\n `other_details` Nullable(String),\n `Grants_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Organisation_Types (\n `organisation_type` Nullable(String),\n `organisation_type_description` Nullable(String),\n `organisation_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Organisations (\n `organisation_id` Nullable(Int64),\n `organisation_type` Nullable(String),\n `organisation_details` Nullable(String),\n `Organisations_description` Nullable(String),\n `organisation_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Outcomes (\n `project_id` Nullable(Int64),\n `outcome_code` Nullable(String),\n `outcome_details` Nullable(String),\n `Project_Outcomes_description` Nullable(String),\n `outcome_details_embedding` Array(Float32)\n);\nCREATE TABLE Project_Staff (\n `staff_id` Nullable(Float64),\n `project_id` Int64,\n `role_code` String,\n `date_from` Nullable(Date),\n `date_to` Nullable(Date),\n `other_details` Nullable(String)\n);\nCREATE TABLE Projects (\n `project_id` Nullable(Int64),\n `organisation_id` Nullable(Int64),\n `project_details` Nullable(String),\n `Projects_description` Nullable(String),\n `project_details_embedding` Array(Float32)\n);\nCREATE TABLE Research_Outcomes (\n `outcome_code` Nullable(String),\n `outcome_description` Nullable(String),\n `outcome_description_embedding` Array(Float32)\n);\nCREATE TABLE Research_Staff (\n `staff_id` Nullable(Int64),\n `employer_organisation_id` Nullable(Int64),\n `staff_details` Nullable(String),\n `Research_Staff_description` Nullable(String),\n `staff_details_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Roles (\n `role_code` Nullable(String),\n `role_description` Nullable(String),\n `role_description_embedding` Array(Float32)\n);\nCREATE TABLE Tasks (\n `task_id` Nullable(Int64),\n `project_id` Nullable(Int64),\n `task_details` Nullable(String),\n `eg_Agree_Objectives` Nullable(String),\n `Tasks_description` Nullable(String),\n `task_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat are the document IDs for the top 5 documents associated with grants over $50,000, that are most relevant to important funding documents?\n\nLet's think step by step!\n" + }, + { + "db_id": "local_govt_and_lot", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'recent medical check-up') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT \n rs.resident_id AS resident_id, \n rs.service_id AS service_id, \n rs.date_requested AS date_requested,\n s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance\n FROM \n Residents_Services rs\n JOIN \n Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT \n r.resident_id AS resident_id, \n r.property_id AS property_id, \n o.organization_id AS organization_id\n FROM \n Residents r\n JOIN \n Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT \n ro.resident_id AS resident_id\nFROM \n Resident_Organization ro\nJOIN \n Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id)\nWHERE \n ro.organization_id = rs.organization_id\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who is one resident recently connected to an organization through a medical service request that aligns with the latest types of medical check-ups?", + "external_knowledge": "The `MATCH` operator used in the query performs an approximate nearest neighbor (ANN) search, which is a method to find entities that are semantically similar to a given concept. Here, the concept is \"recent medical check-up\", and the search retrieves the top 5 most similar service requests using vector embeddings. The embeddings translate textual content into numeric vectors where similarity is gauged by Euclidean distance (L2 norm). The closer the distance, the higher the similarity, allowing the system to infer relatedness beyond exact keyword matches.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'latest medical service request') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'newest types of health check-ups') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'current medical examinations') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'recent healthcare service requests') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'latest medical assessment') AS ref_vec_0,\n\nRecent_Service AS (\n SELECT rs.resident_id, rs.service_id, rs.date_requested, s.organization_id, distance(rs.other_details_embedding, ref_vec_0) AS distance FROM Residents_Services rs JOIN Services s ON toString(rs.service_id) = toString(s.service_id)\n ORDER BY distance\n LIMIT 5\n),\n\nResident_Organization AS (\n SELECT r.resident_id, r.property_id, o.organization_id FROM Residents r JOIN Organizations o ON toString(r.resident_id) = toString(o.parent_organization_id)\n)\n\nSELECT ro.resident_id FROM Resident_Organization ro JOIN Recent_Service rs ON toString(ro.resident_id) = toString(rs.resident_id) WHERE ro.organization_id = rs.organization_id LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Customer_Event_Notes (\n `Customer_Event_Note_ID` Int64,\n `Customer_Event_ID` Int64,\n `service_type_code` String,\n `resident_id` Int64,\n `property_id` Int64,\n `date_moved_in` Date,\n `Customer_Event_Notes_description` Nullable(String)\n);\nCREATE TABLE Customer_Events (\n `Customer_Event_ID` Int64,\n `customer_id` Nullable(Int64),\n `date_moved_in` Nullable(Date),\n `property_id` Nullable(Int64),\n `resident_id` Nullable(Int64),\n `thing_id` Int64\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String),\n `customer_details_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `parent_organization_id` Nullable(Int64),\n `organization_details` Nullable(String),\n `Organizations_description` Nullable(String),\n `organization_details_embedding` Array(Float32)\n);\nCREATE TABLE Organizations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` Nullable(String),\n `property_address` Nullable(String),\n `other_details` Nullable(String),\n `Properties_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Properties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents (\n `resident_id` Nullable(Int64),\n `property_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `date_moved_out` Nullable(String),\n `other_details` Nullable(String),\n `Residents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services (\n `resident_id` Nullable(Int64),\n `service_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `property_id` Nullable(Int64),\n `date_requested` Nullable(String),\n `date_provided` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Services_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Things (\n `thing_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `Type_of_Thing_Code` Nullable(String),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Things_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Things_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Timed_Locations_of_Things (\n `thing_id` Int64,\n `Date_and_Time` Date,\n `Location_Code` String\n);\nCREATE TABLE Timed_Status_of_Things (\n `thing_id` Int64,\n `Date_and_Date` Date,\n `Status_of_Thing_Code` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Customer_Event_Notes (\n `Customer_Event_Note_ID` Int64,\n `Customer_Event_ID` Int64,\n `service_type_code` String,\n `resident_id` Int64,\n `property_id` Int64,\n `date_moved_in` Date,\n `Customer_Event_Notes_description` Nullable(String)\n);\nCREATE TABLE Customer_Events (\n `Customer_Event_ID` Int64,\n `customer_id` Nullable(Int64),\n `date_moved_in` Nullable(Date),\n `property_id` Nullable(Int64),\n `resident_id` Nullable(Int64),\n `thing_id` Int64\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_details` Nullable(String),\n `Customers_description` Nullable(String),\n `customer_details_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `parent_organization_id` Nullable(Int64),\n `organization_details` Nullable(String),\n `Organizations_description` Nullable(String),\n `organization_details_embedding` Array(Float32)\n);\nCREATE TABLE Organizations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Organizations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` Nullable(String),\n `property_address` Nullable(String),\n `other_details` Nullable(String),\n `Properties_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Properties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Properties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents (\n `resident_id` Nullable(Int64),\n `property_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `date_moved_out` Nullable(String),\n `other_details` Nullable(String),\n `Residents_description` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services (\n `resident_id` Nullable(Int64),\n `service_id` Nullable(Int64),\n `date_moved_in` Nullable(String),\n `property_id` Nullable(Int64),\n `date_requested` Nullable(String),\n `date_provided` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Residents_Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Residents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Services_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Services_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Services_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Things (\n `thing_id` Nullable(Int64),\n `organization_id` Nullable(Int64),\n `Type_of_Thing_Code` Nullable(String),\n `service_type_code` Nullable(String),\n `service_details` Nullable(String),\n `Things_description` Nullable(String),\n `service_details_embedding` Array(Float32)\n);\nCREATE TABLE Things_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Things_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Timed_Locations_of_Things (\n `thing_id` Int64,\n `Date_and_Time` Date,\n `Location_Code` String\n);\nCREATE TABLE Timed_Status_of_Things (\n `thing_id` Int64,\n `Date_and_Date` Date,\n `Status_of_Thing_Code` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator used in the query performs an approximate nearest neighbor (ANN) search, which is a method to find entities that are semantically similar to a given concept. Here, the concept is \"recent medical check-up\", and the search retrieves the top 5 most similar service requests using vector embeddings. The embeddings translate textual content into numeric vectors where similarity is gauged by Euclidean distance (L2 norm). The closer the distance, the higher the similarity, allowing the system to infer relatedness beyond exact keyword matches.\nWho is one resident recently connected to an organization through a medical service request that aligns with the latest types of medical check-ups?\n\nLet's think step by step!\n" + }, + { + "db_id": "soccer_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A talented young football player known for his exceptional dribbling and quick acceleration.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance\nFROM Player\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Who are the top 5 football players known for exceptional dribbling and quick acceleration? Provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Football players who excel in dribbling and have remarkable speed.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top footballers famous for their dribbling skills and rapid acceleration.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players known for outstanding dribbling and quick bursts of speed.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Elite footballers recognized for dribbling prowess and fast acceleration.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Football stars with exceptional dribbling ability and swift movement.') AS ref_vec_0\n\nSELECT player_api_id, player_name, distance(Player.Player_description_embedding, ref_vec_0) AS distance FROM Player\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Country (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE League (\n `id` Nullable(Int64),\n `country_id` Nullable(Int64),\n `name` Nullable(String),\n `League_description` Nullable(String),\n `League_description_embedding` Array(Float32)\n);\nCREATE TABLE Player (\n `id` Nullable(Int64),\n `player_api_id` Nullable(Int64),\n `player_name` Nullable(String),\n `player_fifa_api_id` Nullable(Int64),\n `birthday` Nullable(String),\n `height` Nullable(Int64),\n `weight` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Attributes (\n `id` Nullable(Int64),\n `player_fifa_api_id` Nullable(Int64),\n `player_api_id` Nullable(Int64),\n `date` Nullable(String),\n `overall_rating` Nullable(Int64),\n `potential` Nullable(Int64),\n `preferred_foot` Nullable(String),\n `attacking_work_rate` Nullable(String),\n `defensive_work_rate` Nullable(String),\n `crossing` Nullable(Int64),\n `finishing` Nullable(Int64),\n `heading_accuracy` Nullable(Int64),\n `short_passing` Nullable(Int64),\n `volleys` Nullable(Int64),\n `dribbling` Nullable(Int64),\n `curve` Nullable(Int64),\n `free_kick_accuracy` Nullable(Int64),\n `long_passing` Nullable(Int64),\n `ball_control` Nullable(Int64),\n `acceleration` Nullable(Int64),\n `sprint_speed` Nullable(Int64),\n `agility` Nullable(Int64),\n `reactions` Nullable(Int64),\n `balance` Nullable(Int64),\n `shot_power` Nullable(Int64),\n `jumping` Nullable(Int64),\n `stamina` Nullable(Int64),\n `strength` Nullable(Int64),\n `long_shots` Nullable(Int64),\n `aggression` Nullable(Int64),\n `interceptions` Nullable(Int64),\n `positioning` Nullable(Int64),\n `vision` Nullable(Int64),\n `penalties` Nullable(Int64),\n `marking` Nullable(Int64),\n `standing_tackle` Nullable(Int64),\n `sliding_tackle` Nullable(Int64),\n `gk_diving` Nullable(Int64),\n `gk_handling` Nullable(Int64),\n `gk_kicking` Nullable(Int64),\n `gk_positioning` Nullable(Int64),\n `gk_reflexes` Nullable(Int64),\n `Player_Attributes_description` Nullable(String)\n);\nCREATE TABLE Team (\n `id` Nullable(Int64),\n `team_api_id` Nullable(Int64),\n `team_fifa_api_id` Nullable(Int64),\n `team_long_name` Nullable(String),\n `team_short_name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Team_Attributes (\n `id` Nullable(Int64),\n `team_fifa_api_id` Nullable(Int64),\n `team_api_id` Nullable(Int64),\n `date` Nullable(String),\n `buildUpPlaySpeed` Nullable(Int64),\n `buildUpPlaySpeedClass` Nullable(String),\n `buildUpPlayDribbling` Nullable(Int64),\n `buildUpPlayDribblingClass` Nullable(String),\n `buildUpPlayPassing` Nullable(Int64),\n `buildUpPlayPassingClass` Nullable(String),\n `buildUpPlayPositioningClass` Nullable(String),\n `chanceCreationPassing` Nullable(Int64),\n `chanceCreationPassingClass` Nullable(String),\n `chanceCreationCrossing` Nullable(Int64),\n `chanceCreationCrossingClass` Nullable(String),\n `chanceCreationShooting` Nullable(Int64),\n `chanceCreationShootingClass` Nullable(String),\n `chanceCreationPositioningClass` Nullable(String),\n `defencePressure` Nullable(Int64),\n `defencePressureClass` Nullable(String),\n `defenceAggression` Nullable(Int64),\n `defenceAggressionClass` Nullable(String),\n `defenceTeamWidth` Nullable(Int64),\n `defenceTeamWidthClass` Nullable(String),\n `defenceDefenderLineClass` Nullable(String),\n `Team_Attributes_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Country (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Country_description` Nullable(String),\n `Country_description_embedding` Array(Float32)\n);\nCREATE TABLE League (\n `id` Nullable(Int64),\n `country_id` Nullable(Int64),\n `name` Nullable(String),\n `League_description` Nullable(String),\n `League_description_embedding` Array(Float32)\n);\nCREATE TABLE Player (\n `id` Nullable(Int64),\n `player_api_id` Nullable(Int64),\n `player_name` Nullable(String),\n `player_fifa_api_id` Nullable(Int64),\n `birthday` Nullable(String),\n `height` Nullable(Int64),\n `weight` Nullable(Int64),\n `Player_description` Nullable(String),\n `Player_description_embedding` Array(Float32)\n);\nCREATE TABLE Player_Attributes (\n `id` Nullable(Int64),\n `player_fifa_api_id` Nullable(Int64),\n `player_api_id` Nullable(Int64),\n `date` Nullable(String),\n `overall_rating` Nullable(Int64),\n `potential` Nullable(Int64),\n `preferred_foot` Nullable(String),\n `attacking_work_rate` Nullable(String),\n `defensive_work_rate` Nullable(String),\n `crossing` Nullable(Int64),\n `finishing` Nullable(Int64),\n `heading_accuracy` Nullable(Int64),\n `short_passing` Nullable(Int64),\n `volleys` Nullable(Int64),\n `dribbling` Nullable(Int64),\n `curve` Nullable(Int64),\n `free_kick_accuracy` Nullable(Int64),\n `long_passing` Nullable(Int64),\n `ball_control` Nullable(Int64),\n `acceleration` Nullable(Int64),\n `sprint_speed` Nullable(Int64),\n `agility` Nullable(Int64),\n `reactions` Nullable(Int64),\n `balance` Nullable(Int64),\n `shot_power` Nullable(Int64),\n `jumping` Nullable(Int64),\n `stamina` Nullable(Int64),\n `strength` Nullable(Int64),\n `long_shots` Nullable(Int64),\n `aggression` Nullable(Int64),\n `interceptions` Nullable(Int64),\n `positioning` Nullable(Int64),\n `vision` Nullable(Int64),\n `penalties` Nullable(Int64),\n `marking` Nullable(Int64),\n `standing_tackle` Nullable(Int64),\n `sliding_tackle` Nullable(Int64),\n `gk_diving` Nullable(Int64),\n `gk_handling` Nullable(Int64),\n `gk_kicking` Nullable(Int64),\n `gk_positioning` Nullable(Int64),\n `gk_reflexes` Nullable(Int64),\n `Player_Attributes_description` Nullable(String)\n);\nCREATE TABLE Team (\n `id` Nullable(Int64),\n `team_api_id` Nullable(Int64),\n `team_fifa_api_id` Nullable(Int64),\n `team_long_name` Nullable(String),\n `team_short_name` Nullable(String),\n `Team_description` Nullable(String),\n `Team_description_embedding` Array(Float32)\n);\nCREATE TABLE Team_Attributes (\n `id` Nullable(Int64),\n `team_fifa_api_id` Nullable(Int64),\n `team_api_id` Nullable(Int64),\n `date` Nullable(String),\n `buildUpPlaySpeed` Nullable(Int64),\n `buildUpPlaySpeedClass` Nullable(String),\n `buildUpPlayDribbling` Nullable(Int64),\n `buildUpPlayDribblingClass` Nullable(String),\n `buildUpPlayPassing` Nullable(Int64),\n `buildUpPlayPassingClass` Nullable(String),\n `buildUpPlayPositioningClass` Nullable(String),\n `chanceCreationPassing` Nullable(Int64),\n `chanceCreationPassingClass` Nullable(String),\n `chanceCreationCrossing` Nullable(Int64),\n `chanceCreationCrossingClass` Nullable(String),\n `chanceCreationShooting` Nullable(Int64),\n `chanceCreationShootingClass` Nullable(String),\n `chanceCreationPositioningClass` Nullable(String),\n `defencePressure` Nullable(Int64),\n `defencePressureClass` Nullable(String),\n `defenceAggression` Nullable(Int64),\n `defenceAggressionClass` Nullable(String),\n `defenceTeamWidth` Nullable(Int64),\n `defenceTeamWidthClass` Nullable(String),\n `defenceDefenderLineClass` Nullable(String),\n `Team_Attributes_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWho are the top 5 football players known for exceptional dribbling and quick acceleration? Provide their IDs and names.\n\nLet's think step by step!\n" + }, + { + "db_id": "college_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Introduction to Accounting, focuses on basic principles and practices') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance\nFROM CLASS\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which class code and section correspond to an introductory accounting class that focuses on basic principles and practices?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Introductory accounting course covering fundamental principles') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Basic accounting class on core practices and principles') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Principles of accounting introductory course') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Intro to accounting focusing on foundational principles and practices') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Accounting basics class with emphasis on principles and practices') AS ref_vec_0\n\nSELECT CLASS_CODE, CLASS_SECTION, distance(CLASS.CLASS_description_embedding, ref_vec_0) AS distance FROM CLASS\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE CLASS (\n `CLASS_CODE` Nullable(String),\n `CRS_CODE` Nullable(String),\n `CLASS_SECTION` Nullable(String),\n `CLASS_TIME` Nullable(String),\n `CLASS_ROOM` Nullable(String),\n `PROF_NUM` Nullable(Int64),\n `CLASS_description` Nullable(String),\n `CLASS_description_embedding` Array(Float32)\n);\nCREATE TABLE COURSE (\n `CRS_CODE` Nullable(String),\n `DEPT_CODE` Nullable(String),\n `CRS_DESCRIPTION` Nullable(String),\n `CRS_CREDIT` Nullable(Float64),\n `CRS_DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE DEPARTMENT (\n `DEPT_CODE` Nullable(String),\n `DEPT_NAME` Nullable(String),\n `SCHOOL_CODE` Nullable(String),\n `EMP_NUM` Nullable(Int64),\n `DEPT_ADDRESS` Nullable(String),\n `DEPT_EXTENSION` Nullable(String),\n `DEPARTMENT_description` Nullable(String),\n `DEPARTMENT_description_embedding` Array(Float32)\n);\nCREATE TABLE EMPLOYEE (\n `EMP_NUM` Nullable(Int64),\n `EMP_LNAME` Nullable(String),\n `EMP_FNAME` Nullable(String),\n `EMP_INITIAL` Nullable(String),\n `EMP_JOBCODE` Nullable(String),\n `EMP_HIREDATE` Nullable(String),\n `EMP_DOB` Nullable(String),\n `EMPLOYEE_description` Nullable(String),\n `EMPLOYEE_description_embedding` Array(Float32)\n);\nCREATE TABLE ENROLL (\n `CLASS_CODE` Nullable(String),\n `STU_NUM` Nullable(Int64),\n `ENROLL_GRADE` Nullable(String)\n);\nCREATE TABLE PROFESSOR (\n `EMP_NUM` Nullable(Int64),\n `DEPT_CODE` Nullable(String),\n `PROF_OFFICE` Nullable(String),\n `PROF_EXTENSION` Nullable(String),\n `PROF_HIGH_DEGREE` Nullable(String),\n `PROFESSOR_description` Nullable(String),\n `PROFESSOR_description_embedding` Array(Float32)\n);\nCREATE TABLE STUDENT (\n `STU_NUM` Nullable(Int64),\n `STU_LNAME` Nullable(String),\n `STU_FNAME` Nullable(String),\n `STU_INIT` Nullable(String),\n `STU_DOB` Nullable(String),\n `STU_HRS` Nullable(Int64),\n `STU_CLASS` Nullable(String),\n `STU_GPA` Nullable(Float64),\n `STU_TRANSFER` Nullable(Float64),\n `DEPT_CODE` Nullable(String),\n `STU_PHONE` Nullable(String),\n `PROF_NUM` Nullable(Int64),\n `STUDENT_description` Nullable(String),\n `STUDENT_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE CLASS (\n `CLASS_CODE` Nullable(String),\n `CRS_CODE` Nullable(String),\n `CLASS_SECTION` Nullable(String),\n `CLASS_TIME` Nullable(String),\n `CLASS_ROOM` Nullable(String),\n `PROF_NUM` Nullable(Int64),\n `CLASS_description` Nullable(String),\n `CLASS_description_embedding` Array(Float32)\n);\nCREATE TABLE COURSE (\n `CRS_CODE` Nullable(String),\n `DEPT_CODE` Nullable(String),\n `CRS_DESCRIPTION` Nullable(String),\n `CRS_CREDIT` Nullable(Float64),\n `CRS_DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE DEPARTMENT (\n `DEPT_CODE` Nullable(String),\n `DEPT_NAME` Nullable(String),\n `SCHOOL_CODE` Nullable(String),\n `EMP_NUM` Nullable(Int64),\n `DEPT_ADDRESS` Nullable(String),\n `DEPT_EXTENSION` Nullable(String),\n `DEPARTMENT_description` Nullable(String),\n `DEPARTMENT_description_embedding` Array(Float32)\n);\nCREATE TABLE EMPLOYEE (\n `EMP_NUM` Nullable(Int64),\n `EMP_LNAME` Nullable(String),\n `EMP_FNAME` Nullable(String),\n `EMP_INITIAL` Nullable(String),\n `EMP_JOBCODE` Nullable(String),\n `EMP_HIREDATE` Nullable(String),\n `EMP_DOB` Nullable(String),\n `EMPLOYEE_description` Nullable(String),\n `EMPLOYEE_description_embedding` Array(Float32)\n);\nCREATE TABLE ENROLL (\n `CLASS_CODE` Nullable(String),\n `STU_NUM` Nullable(Int64),\n `ENROLL_GRADE` Nullable(String)\n);\nCREATE TABLE PROFESSOR (\n `EMP_NUM` Nullable(Int64),\n `DEPT_CODE` Nullable(String),\n `PROF_OFFICE` Nullable(String),\n `PROF_EXTENSION` Nullable(String),\n `PROF_HIGH_DEGREE` Nullable(String),\n `PROFESSOR_description` Nullable(String),\n `PROFESSOR_description_embedding` Array(Float32)\n);\nCREATE TABLE STUDENT (\n `STU_NUM` Nullable(Int64),\n `STU_LNAME` Nullable(String),\n `STU_FNAME` Nullable(String),\n `STU_INIT` Nullable(String),\n `STU_DOB` Nullable(String),\n `STU_HRS` Nullable(Int64),\n `STU_CLASS` Nullable(String),\n `STU_GPA` Nullable(Float64),\n `STU_TRANSFER` Nullable(Float64),\n `DEPT_CODE` Nullable(String),\n `STU_PHONE` Nullable(String),\n `PROF_NUM` Nullable(Int64),\n `STUDENT_description` Nullable(String),\n `STUDENT_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me which class code and section correspond to an introductory accounting class that focuses on basic principles and practices?\n\nLet's think step by step!\n" + }, + { + "db_id": "party_people", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Released in the United States with a focus on regional policies and governance.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance\nFROM region r\nJOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the names of regions and their corresponding political parties for the top 10 regions that are most aligned with the concept of being released in the United States with a focus on regional policies and governance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Regions in the US emphasizing regional governance and policy alignment.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'US regions with a strong focus on regional policy and governance.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top US regions aligned with policies focused on regional governance.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Regions prioritizing governance and policy within the US context.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'US regions where regional policies and governance are highly emphasized.') AS ref_vec_0\n\nSELECT r.Region_name, p.Party_name, distance(r.region_description_embedding, ref_vec_0) AS distance FROM region r JOIN party p ON toString(r.Region_ID) = toString(p.Region_ID)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Member_Name` Nullable(String),\n `Party_ID` Nullable(String),\n `In_office` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Minister` Nullable(String),\n `Took_office` Nullable(String),\n `Left_office` Nullable(String),\n `Region_ID` Nullable(Int64),\n `Party_name` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);\nCREATE TABLE party_events (\n `Event_ID` Nullable(Int64),\n `Event_Name` Nullable(String),\n `Party_ID` Nullable(Int64),\n `Member_in_charge_ID` Nullable(Int64),\n `party_events_description` Nullable(String),\n `party_events_description_embedding` Array(Float32)\n);\nCREATE TABLE region (\n `Region_ID` Nullable(Int64),\n `Region_name` Nullable(String),\n `Date` Nullable(String),\n `Label` Nullable(String),\n `Format` Nullable(String),\n `Catalogue` Nullable(String),\n `region_description` Nullable(String),\n `region_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Member_Name` Nullable(String),\n `Party_ID` Nullable(String),\n `In_office` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Minister` Nullable(String),\n `Took_office` Nullable(String),\n `Left_office` Nullable(String),\n `Region_ID` Nullable(Int64),\n `Party_name` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);\nCREATE TABLE party_events (\n `Event_ID` Nullable(Int64),\n `Event_Name` Nullable(String),\n `Party_ID` Nullable(Int64),\n `Member_in_charge_ID` Nullable(Int64),\n `party_events_description` Nullable(String),\n `party_events_description_embedding` Array(Float32)\n);\nCREATE TABLE region (\n `Region_ID` Nullable(Int64),\n `Region_name` Nullable(String),\n `Date` Nullable(String),\n `Label` Nullable(String),\n `Format` Nullable(String),\n `Catalogue` Nullable(String),\n `region_description` Nullable(String),\n `region_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the names of regions and their corresponding political parties for the top 10 regions that are most aligned with the concept of being released in the United States with a focus on regional policies and governance?\n\nLet's think step by step!\n" + }, + { + "db_id": "race_track", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Daytona 500, a famous NASCAR race, happening in February at Track 3.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance \nFROM race\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you tell me which race seems to line up with the description of that well-known February NASCAR event at Track 3?", + "external_knowledge": "The use of vector embeddings, like the `all-MiniLM-L6-v2`, allows for the comparison of textual data by translating them into numerical vectors. The `MATCH` operator is used to find approximate nearest neighbors based on vector similarity, typically using Euclidean distance. In this context, the query is looking for textual similarities rather than exact matches, enabling more nuanced searches. The phrase \"well-known February NASCAR event\" refers to the Daytona 500, which is an iconic race that takes place annually in February.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The well-known NASCAR event in February at Track 3, commonly referred to as the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The famous February NASCAR race at Track 3, known as the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Track 3 hosts a major NASCAR event every February, famously called the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Daytona 500, a prominent NASCAR race occurring in February at Track 3.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In February, Track 3 features a renowned race known as the Daytona 500.') AS ref_vec_0\n\nSELECT Race_ID, distance(race.race_description_embedding, ref_vec_0) AS distance FROM race\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE race (\n `Race_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Class` Nullable(String),\n `Date` Nullable(String),\n `Track_ID` Nullable(String),\n `race_description` Nullable(String),\n `race_description_embedding` Array(Float32)\n);\nCREATE TABLE track (\n `Track_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Location` Nullable(String),\n `Seating` Nullable(Float64),\n `Year_Opened` Nullable(Float64),\n `track_description` Nullable(String),\n `track_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE race (\n `Race_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Class` Nullable(String),\n `Date` Nullable(String),\n `Track_ID` Nullable(String),\n `race_description` Nullable(String),\n `race_description_embedding` Array(Float32)\n);\nCREATE TABLE track (\n `Track_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Location` Nullable(String),\n `Seating` Nullable(Float64),\n `Year_Opened` Nullable(Float64),\n `track_description` Nullable(String),\n `track_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe use of vector embeddings, like the `all-MiniLM-L6-v2`, allows for the comparison of textual data by translating them into numerical vectors. The `MATCH` operator is used to find approximate nearest neighbors based on vector similarity, typically using Euclidean distance. In this context, the query is looking for textual similarities rather than exact matches, enabling more nuanced searches. The phrase \"well-known February NASCAR event\" refers to the Daytona 500, which is an iconic race that takes place annually in February.\nCan you tell me which race seems to line up with the description of that well-known February NASCAR event at Track 3?\n\nLet's think step by step!\n" + }, + { + "db_id": "flight_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Long-haul flight from New York to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft suitable for long international flights') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance\n FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance\n FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName\nFROM SimilarFlights sf\nJOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid)\nORDER BY sf.distance, sa.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the ten flights that best match the description of a long-haul journey from New York to London and pair them with the five aircraft most suitable for long international flights. List the flight numbers and corresponding aircraft names, ordered by their similarity distances.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Transatlantic journey from NYC to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft designed for long-haul international routes') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Extended distance flight from New York to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft optimal for long-distance international travel') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Long-distance flight from NYC to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft ideal for lengthy international journeys') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Intercontinental flight from New York to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft best for long international flights') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Long-haul journey across the Atlantic from NYC to London') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Aircraft suitable for extended international flights') AS ref_vec_1,\n\nflight_filtered AS (\n SELECT\n *,\n distance(flight_description_embedding, ref_vec_0) AS distance\n FROM flight\n\n ORDER BY distance\n LIMIT 10\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarFlights AS (\n SELECT flno, origin, destination, distance_val, price, aid, distance FROM flight_filtered AS flight\n),\n\nSimilarAircraft AS (\n SELECT aid, name, distance_val, distance FROM aircraft_filtered AS aircraft\n)\n\nSELECT sf.flno AS FlightNumber, sa.name AS AircraftName FROM SimilarFlights sf JOIN SimilarAircraft sa ON toString(sf.aid) = toString(sa.aid) ORDER BY sf.distance, sa.distance;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `aid` Nullable(Int64),\n `name` Nullable(String),\n `distance_val` Nullable(Int64),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE certificate (\n `eid` Nullable(String),\n `aid` Nullable(String)\n);\nCREATE TABLE employee (\n `eid` Nullable(Int64),\n `name` Nullable(String),\n `salary` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `flno` Nullable(Int64),\n `origin` Nullable(String),\n `destination` Nullable(String),\n `distance_val` Nullable(Int64),\n `departure_date` Nullable(String),\n `arrival_date` Nullable(String),\n `price` Nullable(Int64),\n `aid` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE aircraft (\n `aid` Nullable(Int64),\n `name` Nullable(String),\n `distance_val` Nullable(Int64),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE certificate (\n `eid` Nullable(String),\n `aid` Nullable(String)\n);\nCREATE TABLE employee (\n `eid` Nullable(Int64),\n `name` Nullable(String),\n `salary` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `flno` Nullable(Int64),\n `origin` Nullable(String),\n `destination` Nullable(String),\n `distance_val` Nullable(Int64),\n `departure_date` Nullable(String),\n `arrival_date` Nullable(String),\n `price` Nullable(Int64),\n `aid` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the ten flights that best match the description of a long-haul journey from New York to London and pair them with the five aircraft most suitable for long international flights. List the flight numbers and corresponding aircraft names, ordered by their similarity distances.\n\nLet's think step by step!\n" + }, + { + "db_id": "dorm_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old student majoring in computer science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dorm with a capacity of 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance\n FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance\n FROM Dorm_filtered AS Dorm\n)\n\nSELECT \n s.StuID AS StuID, \n d.dormid AS dormid\nFROM \n StudentMatches s\nJOIN \n Lives_in l ON toString(s.StuID) = toString(l.stuid)\nJOIN \n DormMatches d ON toString(l.dormid) = toString(d.dormid)\nORDER BY \n s.student_distance + d.dorm_distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 4, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "**\n\nPlease provide the student IDs and dormitory IDs for the top 10 student-dormitory pairs where the students are described as 18-year-old computer science majors, and the dormitories are described as having a capacity for 100 male students.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old computer science student') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dormitory for 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'computer science major, 18 years old') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'accommodation for 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'student, 18, studying computer science') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dorm for 100 males') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old major in CS') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'housing for 100 male students') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', '18-year-old studying CS') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'dormitory with capacity for 100 males') AS ref_vec_1,\n\nStudent_filtered AS (\n SELECT\n *,\n distance(Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n\n ORDER BY distance\n LIMIT 5\n),\n\nDorm_filtered AS (\n SELECT\n *,\n distance(Dorm_description_embedding, ref_vec_1) AS distance\n FROM Dorm\n\n ORDER BY distance\n LIMIT 5\n),\n\nStudentMatches AS (\n SELECT StuID, distance AS student_distance FROM Student_filtered AS Student\n),\n\nDormMatches AS (\n SELECT dormid, distance AS dorm_distance FROM Dorm_filtered AS Dorm\n)\n\nSELECT s.StuID, d.dormid FROM StudentMatches s JOIN Lives_in l ON toString(s.StuID) = toString(l.stuid) JOIN DormMatches d ON toString(l.dormid) = toString(d.dormid) ORDER BY s.student_distance + d.dorm_distance LIMIT 10;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Dorm (\n `dormid` Nullable(Int64),\n `dorm_name` Nullable(String),\n `student_capacity` Nullable(Int64),\n `gender` Nullable(String),\n `Dorm_description` Nullable(String),\n `Dorm_description_embedding` Array(Float32)\n);\nCREATE TABLE Dorm_amenity (\n `amenid` Nullable(Int64),\n `amenity_name` Nullable(String),\n `Dorm_amenity_description` Nullable(String),\n `Dorm_amenity_description_embedding` Array(Float32)\n);\nCREATE TABLE Has_amenity (\n `dormid` Nullable(Int64),\n `amenid` Nullable(Int64)\n);\nCREATE TABLE Lives_in (\n `stuid` Nullable(Int64),\n `dormid` Nullable(Int64),\n `room_number` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Dorm (\n `dormid` Nullable(Int64),\n `dorm_name` Nullable(String),\n `student_capacity` Nullable(Int64),\n `gender` Nullable(String),\n `Dorm_description` Nullable(String),\n `Dorm_description_embedding` Array(Float32)\n);\nCREATE TABLE Dorm_amenity (\n `amenid` Nullable(Int64),\n `amenity_name` Nullable(String),\n `Dorm_amenity_description` Nullable(String),\n `Dorm_amenity_description_embedding` Array(Float32)\n);\nCREATE TABLE Has_amenity (\n `dormid` Nullable(Int64),\n `amenid` Nullable(Int64)\n);\nCREATE TABLE Lives_in (\n `stuid` Nullable(Int64),\n `dormid` Nullable(Int64),\n `room_number` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n**\n\nPlease provide the student IDs and dormitory IDs for the top 10 student-dormitory pairs where the students are described as 18-year-old computer science majors, and the dormitories are described as having a capacity for 100 male students.\n\n**\n\nLet's think step by step!\n" + }, + { + "db_id": "machine_repair", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'experienced technician in the NY team') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'complex repair procedure for launch vehicle') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'high-performance motorcycle from Marlboro Pileri') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance\n FROM t_filtered AS t\n ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance\n FROM r_filtered AS r\n ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance\n FROM m_filtered AS m\n ORDER BY distance\n)\n\nSELECT ra.technician_id\nFROM repair_assignment ra\nJOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id)\nJOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID)\nJOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID)\nORDER BY tm.distance + rm.distance + mm.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Who is the technician assigned to a repair involving a high-performance Marlboro Pileri motorcycle and a complex launch vehicle procedure in the NY team?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'technician specializing in NY high-performance vehicle repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'detailed procedure for complex launch vehicle repair') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri high-speed motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'NY team technician with expertise in complex repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'launch vehicle repair involving complex procedures') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'high-performance Marlboro Pileri motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'expert technician from NY specializing in vehicle procedures') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'complex launch vehicle repair task') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri high-performance motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'NY-based technician for high-performance vehicle repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'repair procedure for complex launch vehicle') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri motorcycle with high performance') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'technician in NY team skilled in high-performance repairs') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'complex procedures for launch vehicle repair') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Marlboro Pileri high-performance motorcycle') AS ref_vec_2,\n\nt_filtered AS (\n SELECT\n *,\n distance(technician_description_embedding, ref_vec_0) AS distance\n FROM technician\n\n ORDER BY distance\n LIMIT 5\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(repair_description_embedding, ref_vec_1) AS distance\n FROM repair\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(machine_description_embedding, ref_vec_2) AS distance\n FROM machine\n\n ORDER BY distance\n LIMIT 5\n),\n\nTechnicianMatches AS (\n SELECT t.technician_id, t.Name, t.Age, distance FROM t_filtered AS t ORDER BY distance\n),\n\nRepairMatches AS (\n SELECT r.repair_ID, r.name, r.Launch_Date, distance FROM r_filtered AS r ORDER BY distance\n),\n\nMachineMatches AS (\n SELECT m.Machine_ID, m.machine_description, distance FROM m_filtered AS m ORDER BY distance\n)\n\nSELECT ra.technician_id FROM repair_assignment ra JOIN TechnicianMatches tm ON toString(ra.technician_id) = toString(tm.technician_id) JOIN RepairMatches rm ON toString(ra.repair_ID) = toString(rm.repair_ID) JOIN MachineMatches mm ON toString(ra.Machine_ID) = toString(mm.Machine_ID) ORDER BY tm.distance + rm.distance + mm.distance LIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE machine (\n `Machine_ID` Nullable(Int64),\n `Making_Year` Nullable(Int64),\n `Class` Nullable(String),\n `Team` Nullable(String),\n `Machine_series` Nullable(String),\n `value_points` Nullable(Float64),\n `quality_rank` Nullable(Int64),\n `machine_description` Nullable(String),\n `machine_description_embedding` Array(Float32)\n);\nCREATE TABLE machine_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE repair (\n `repair_ID` Nullable(Int64),\n `name` Nullable(String),\n `Launch_Date` Nullable(String),\n `Notes` Nullable(String),\n `repair_description` Nullable(String),\n `Notes_embedding` Array(Float32),\n `repair_description_embedding` Array(Float32)\n);\nCREATE TABLE repair_assignment (\n `technician_id` Nullable(Int64),\n `repair_ID` Nullable(Int64),\n `Machine_ID` Nullable(Int64)\n);\nCREATE TABLE repair_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE repair_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE technician (\n `technician_id` Nullable(Float64),\n `Name` Nullable(String),\n `Team` Nullable(String),\n `Starting_Year` Nullable(Float64),\n `Age` Nullable(Int64),\n `technician_description` Nullable(String),\n `technician_description_embedding` Array(Float32)\n);\nCREATE TABLE technician_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE machine (\n `Machine_ID` Nullable(Int64),\n `Making_Year` Nullable(Int64),\n `Class` Nullable(String),\n `Team` Nullable(String),\n `Machine_series` Nullable(String),\n `value_points` Nullable(Float64),\n `quality_rank` Nullable(Int64),\n `machine_description` Nullable(String),\n `machine_description_embedding` Array(Float32)\n);\nCREATE TABLE machine_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE machine_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE repair (\n `repair_ID` Nullable(Int64),\n `name` Nullable(String),\n `Launch_Date` Nullable(String),\n `Notes` Nullable(String),\n `repair_description` Nullable(String),\n `Notes_embedding` Array(Float32),\n `repair_description_embedding` Array(Float32)\n);\nCREATE TABLE repair_assignment (\n `technician_id` Nullable(Int64),\n `repair_ID` Nullable(Int64),\n `Machine_ID` Nullable(Int64)\n);\nCREATE TABLE repair_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE repair_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE repair_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE technician (\n `technician_id` Nullable(Float64),\n `Name` Nullable(String),\n `Team` Nullable(String),\n `Starting_Year` Nullable(Float64),\n `Age` Nullable(Int64),\n `technician_description` Nullable(String),\n `technician_description_embedding` Array(Float32)\n);\nCREATE TABLE technician_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE technician_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWho is the technician assigned to a repair involving a high-performance Marlboro Pileri motorcycle and a complex launch vehicle procedure in the NY team?\n\nLet's think step by step!\n" + }, + { + "db_id": "music_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A soulful melody with deep lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance\n FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance\n FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance\n FROM files_filtered AS files\n)\n\nSELECT s.song_name\nFROM SimilarSongs s\nJOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name)\nJOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id)\nORDER BY s.song_distance + g.genre_distance + f.file_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you identify the song that best fits a \"soulful melody with deep lyrics,\" belongs to a \"popular genre with high rating,\" and is associated with a \"high-quality audio file with long duration\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A heartfelt tune with profound words') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An emotional melody with meaningful lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A moving melody with deep and thoughtful lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n WHERE song_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'A moving melody with deep AND thoughtful lyrics')\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A song with a soulful tune and insightful lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n WHERE song_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'A song with a soulful tune AND insightful lyrics')\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A touching melody with significant lyrics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Popular genre with high rating') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'High-quality audio file with long duration') AS ref_vec_2,\n\nsong_filtered AS (\n SELECT\n *,\n distance(song_description_embedding, ref_vec_0) AS distance\n FROM song\n\n ORDER BY distance\n LIMIT 5\n),\n\ngenre_filtered AS (\n SELECT\n *,\n distance(genre_description_embedding, ref_vec_1) AS distance\n FROM genre\n\n ORDER BY distance\n LIMIT 5\n),\n\nfiles_filtered AS (\n SELECT\n *,\n distance(files_description_embedding, ref_vec_2) AS distance\n FROM files\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarSongs AS (\n SELECT song_name, artist_name, genre_is, f_id, distance AS song_distance FROM song_filtered AS song\n),\n\nSimilarGenres AS (\n SELECT g_name, distance AS genre_distance FROM genre_filtered AS genre\n),\n\nSimilarFiles AS (\n SELECT f_id, distance AS file_distance FROM files_filtered AS files\n)\n\nSELECT s.song_name FROM SimilarSongs s JOIN SimilarGenres g ON toString(s.genre_is) = toString(g.g_name) JOIN SimilarFiles f ON toString(s.f_id) = toString(f.f_id) ORDER BY s.song_distance + g.genre_distance + f.file_distance LIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `artist_name` Nullable(String),\n `country` Nullable(String),\n `gender` Nullable(String),\n `preferred_genre` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE files (\n `f_id` Nullable(Int64),\n `artist_name` Nullable(String),\n `file_size` Nullable(String),\n `duration` Nullable(String),\n `formats` Nullable(String),\n `files_description` Nullable(String),\n `files_description_embedding` Array(Float32)\n);\nCREATE TABLE genre (\n `g_name` Nullable(String),\n `rating` Nullable(String),\n `most_popular_in` Nullable(String),\n `genre_description` Nullable(String),\n `genre_description_embedding` Array(Float32)\n);\nCREATE TABLE song (\n `song_name` Nullable(String),\n `artist_name` Nullable(String),\n `country` Nullable(String),\n `f_id` Nullable(Int64),\n `genre_is` Nullable(String),\n `rating` Nullable(Int64),\n `languages` Nullable(String),\n `releasedate` Nullable(String),\n `resolution` Nullable(Int64),\n `song_description` Nullable(String),\n `song_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE artist (\n `artist_name` Nullable(String),\n `country` Nullable(String),\n `gender` Nullable(String),\n `preferred_genre` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE files (\n `f_id` Nullable(Int64),\n `artist_name` Nullable(String),\n `file_size` Nullable(String),\n `duration` Nullable(String),\n `formats` Nullable(String),\n `files_description` Nullable(String),\n `files_description_embedding` Array(Float32)\n);\nCREATE TABLE genre (\n `g_name` Nullable(String),\n `rating` Nullable(String),\n `most_popular_in` Nullable(String),\n `genre_description` Nullable(String),\n `genre_description_embedding` Array(Float32)\n);\nCREATE TABLE song (\n `song_name` Nullable(String),\n `artist_name` Nullable(String),\n `country` Nullable(String),\n `f_id` Nullable(Int64),\n `genre_is` Nullable(String),\n `rating` Nullable(Int64),\n `languages` Nullable(String),\n `releasedate` Nullable(String),\n `resolution` Nullable(Int64),\n `song_description` Nullable(String),\n `song_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you identify the song that best fits a \"soulful melody with deep lyrics,\" belongs to a \"popular genre with high rating,\" and is associated with a \"high-quality audio file with long duration\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_software_problems", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Dedicated technical support and problem-solving expertise.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date\n FROM Problem_Log\n WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance\nFROM Staff s\nJOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to identify the first names of the top 10 staff members who have recently been assigned to problem logs within the last 30 days and whose profiles closely match the description of dedicated technical support and problem-solving expertise.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Expert in technical support and problem resolution.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Technical support specialist with problem-solving skills.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Proficient in handling technical issues and solutions.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Skilled in technical troubleshooting and support.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dedicated to resolving technical problems efficiently.') AS ref_vec_0,\n\nRecent_Problem_Logs AS (\n SELECT problem_log_id, assigned_to_staff_id, problem_id, log_entry_date FROM Problem_Log WHERE log_entry_date > date_sub(DAY, 30, now())\n)\n\nSELECT s.staff_first_name, distance(s.Staff_description_embedding, ref_vec_0) AS distance FROM Staff s JOIN Recent_Problem_Logs rpl ON toString(s.staff_id) = toString(rpl.assigned_to_staff_id)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI need to identify the first names of the top 10 staff members who have recently been assigned to problem logs within the last 30 days and whose profiles closely match the description of dedicated technical support and problem-solving expertise.\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_software_problems", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A query about interface design issues') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance \nFROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a problem category that has something to do with interface design challenges?", + "external_knowledge": "The `MATCH` operator in the query utilizes vector embedding for performing a semantic similarity search, which is not based on exact matches but rather on capturing the meaning and context of the input text. The embeddings are typically compared using Euclidean distance, where a smaller distance indicates higher similarity. The `lembed()` function generates vector embeddings using the model 'all-MiniLM-L6-v2' from a given text input to enable this search. The query limits the result to the single most relevant entry, highlighting the top problem category related to interface design issues.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Interface design challenges') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Issues related to UI design') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Problems in user interface creation') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Challenges in designing interfaces') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Design difficulties in UI development') AS ref_vec_0\n\nSELECT problem_category_code, problem_category_description, distance(Problem_Category_Codes.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in the query utilizes vector embedding for performing a semantic similarity search, which is not based on exact matches but rather on capturing the meaning and context of the input text. The embeddings are typically compared using Euclidean distance, where a smaller distance indicates higher similarity. The `lembed()` function generates vector embeddings using the model 'all-MiniLM-L6-v2' from a given text input to enable this search. The query limits the result to the single most relevant entry, highlighting the top problem category related to interface design issues.\nCan you find a problem category that has something to do with interface design challenges?\n\nLet's think step by step!\n" + }, + { + "db_id": "medicine_enzyme_interaction", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase enzyme found in mitochondrion') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An FDA approved medicine used for treatment') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance\n FROM e_filtered AS e\n ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance\n FROM m_filtered AS m\n ORDER BY m.distance\n)\n\nSELECT mei.interaction_type\nFROM medicine_enzyme_interaction mei\nJOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id)\nJOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id)\nORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you please identify the type of interaction between the top 5 enzymes characterized as \"ALA synthase enzyme found in mitochondrion\" and the top 5 FDA-approved medicines used for treatment? Please ensure that the interaction is the most relevant based on the average similarity of their descriptions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase located in mitochondria') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A treatment-related FDA approved drug') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Mitochondrial ALA synthase enzyme') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA approved medication for therapy') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase enzyme within mitochondria') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA approved therapeutic medicine') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Mitochondria ALA synthase enzyme') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA authorized drug for treatment') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'ALA synthase enzyme found in mitochondrial region') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'FDA sanctioned medicine for therapy') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(enzyme_description_embedding, ref_vec_0) AS distance\n FROM enzyme\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medicine_description_embedding, ref_vec_1) AS distance\n FROM medicine\n\n ORDER BY distance\n LIMIT 5\n),\n\nEnzymeCandidates AS (\n SELECT e.id, e.distance AS enzyme_distance FROM e_filtered AS e ORDER BY e.distance\n),\n\nMedicineCandidates AS (\n SELECT m.id, m.distance AS medicine_distance FROM m_filtered AS m ORDER BY m.distance\n)\n\nSELECT mei.interaction_type FROM medicine_enzyme_interaction mei JOIN EnzymeCandidates ec ON toString(mei.enzyme_id) = toString(ec.id) JOIN MedicineCandidates mc ON toString(mei.medicine_id) = toString(mc.id) ORDER BY (ec.enzyme_distance + mc.medicine_distance) / 2 LIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE enzyme (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Location` Nullable(String),\n `Product` Nullable(String),\n `Chromosome` Nullable(String),\n `OMIM` Nullable(Int64),\n `Porphyria` Nullable(String),\n `enzyme_description` Nullable(String),\n `enzyme_description_embedding` Array(Float32)\n);\nCREATE TABLE medicine (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Trade_Name` Nullable(String),\n `FDA_approved` Nullable(String),\n `medicine_description` Nullable(String),\n `medicine_description_embedding` Array(Float32)\n);\nCREATE TABLE medicine_enzyme_interaction (\n `enzyme_id` Nullable(Int64),\n `medicine_id` Nullable(Int64),\n `interaction_type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE enzyme (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Location` Nullable(String),\n `Product` Nullable(String),\n `Chromosome` Nullable(String),\n `OMIM` Nullable(Int64),\n `Porphyria` Nullable(String),\n `enzyme_description` Nullable(String),\n `enzyme_description_embedding` Array(Float32)\n);\nCREATE TABLE medicine (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Trade_Name` Nullable(String),\n `FDA_approved` Nullable(String),\n `medicine_description` Nullable(String),\n `medicine_description_embedding` Array(Float32)\n);\nCREATE TABLE medicine_enzyme_interaction (\n `enzyme_id` Nullable(Int64),\n `medicine_id` Nullable(Int64),\n `interaction_type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please identify the type of interaction between the top 5 enzymes characterized as \"ALA synthase enzyme found in mitochondrion\" and the top 5 FDA-approved medicines used for treatment? Please ensure that the interaction is the most relevant based on the average similarity of their descriptions.\n\nLet's think step by step!\n" + }, + { + "db_id": "allergy_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is a common environmental allergy.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance \nFROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I want to know which allergy type is identified as most similar to the concept of pollen being a common environmental allergy.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is often recognized as a typical environmental allergen.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is frequently associated with environmental allergies.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is a prevalent environmental allergy trigger.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pollen is commonly linked to environmental allergy reactions.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Environmental allergies often include pollen as a major factor.') AS ref_vec_0\n\nSELECT Allergy, distance(Allergy_Type.Allergy_Type_description_embedding, ref_vec_0) AS distance FROM Allergy_Type\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Allergy_Type (\n `Allergy` Nullable(String),\n `AllergyType` Nullable(String),\n `Allergy_Type_description` Nullable(String),\n `Allergy_Type_description_embedding` Array(Float32)\n);\nCREATE TABLE Has_Allergy (\n `StuID` Nullable(Int64),\n `Allergy` Nullable(String)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Allergy_Type (\n `Allergy` Nullable(String),\n `AllergyType` Nullable(String),\n `Allergy_Type_description` Nullable(String),\n `Allergy_Type_description_embedding` Array(Float32)\n);\nCREATE TABLE Has_Allergy (\n `StuID` Nullable(Int64),\n `Allergy` Nullable(String)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI want to know which allergy type is identified as most similar to the concept of pollen being a common environmental allergy.\n\nLet's think step by step!\n" + }, + { + "db_id": "chinook_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Famous rock album with iconic songs') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance\nFROM Album a\nJOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the titles of the top 5 albums that are well-known for their iconic rock songs and the names of the artists who created them?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary rock albums with memorable tracks') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top rock albums known for their standout songs') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Iconic albums with famous rock songs') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned rock albums with classic hits') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Celebrated rock albums featuring iconic tracks') AS ref_vec_0\n\nSELECT a.Title, ar.Name, distance(a.Album_description_embedding, ref_vec_0) AS distance FROM Album a JOIN Artist ar ON toString(a.ArtistId) = toString(ar.ArtistId)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Album (\n `AlbumId` Nullable(Int64),\n `Title` Nullable(String),\n `ArtistId` Nullable(Int64),\n `Album_description` Nullable(String),\n `Album_description_embedding` Array(Float32)\n);\nCREATE TABLE Artist (\n `ArtistId` Nullable(Int64),\n `Name` Nullable(String),\n `Artist_description` Nullable(String),\n `Artist_description_embedding` Array(Float32)\n);\nCREATE TABLE Customer (\n `CustomerId` Nullable(Int64),\n `FirstName` Nullable(String),\n `LastName` Nullable(String),\n `Company` Nullable(String),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `SupportRepId` Nullable(Int64),\n `Customer_description` Nullable(String),\n `Customer_description_embedding` Array(Float32)\n);\nCREATE TABLE Employee (\n `EmployeeId` Int64,\n `LastName` String,\n `FirstName` String,\n `Title` Nullable(String),\n `ReportsTo` Nullable(Int64),\n `BirthDate` Nullable(Date),\n `HireDate` Nullable(Date),\n `Address` Nullable(String),\n `City` Nullable(String),\n `State` Nullable(String),\n `Country` Nullable(String),\n `PostalCode` Nullable(String),\n `Phone` Nullable(String),\n `Fax` Nullable(String),\n `Email` Nullable(String),\n `Employee_description` Nullable(String)\n);\nCREATE TABLE Genre (\n `GenreId` Nullable(Int64),\n `Name` Nullable(String),\n `Genre_description` Nullable(String),\n `Genre_description_embedding` Array(Float32)\n);\nCREATE TABLE Invoice (\n `InvoiceId` Nullable(Int64),\n `CustomerId` Nullable(Int64),\n `InvoiceDate` Nullable(String),\n `BillingAddress` Nullable(String),\n `BillingCity` Nullable(String),\n `BillingState` Nullable(String),\n `BillingCountry` Nullable(String),\n `BillingPostalCode` Nullable(String),\n `Total` Nullable(Float64),\n `Invoice_description` Nullable(String),\n `Invoice_description_embedding` Array(Float32)\n);\nCREATE TABLE InvoiceLine (\n `InvoiceLineId` Int64,\n `InvoiceId` Int64,\n `TrackId` Int64,\n `UnitPrice` Decimal(38, 6),\n `Quantity` Int64\n);\nCREATE TABLE MediaType (\n `MediaTypeId` Int64,\n `Name` Nullable(String)\n);\nCREATE TABLE Playlist (\n `PlaylistId` Nullable(Int64),\n `Name` Nullable(String),\n `Playlist_description` Nullable(String),\n `Playlist_description_embedding` Array(Float32)\n);\nCREATE TABLE PlaylistTrack (\n `PlaylistId` Int64,\n `TrackId` Int64\n);\nCREATE TABLE Track (\n `TrackId` Nullable(Int64),\n `Name` Nullable(String),\n `AlbumId` Nullable(Int64),\n `MediaTypeId` Nullable(Int64),\n `GenreId` Nullable(Int64),\n `Composer` Nullable(String),\n `Milliseconds` Nullable(Int64),\n `Bytes` Nullable(Int64),\n `UnitPrice` Nullable(Float64),\n `Track_description` Nullable(String),\n `Track_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the titles of the top 5 albums that are well-known for their iconic rock songs and the names of the artists who created them?\n\nLet's think step by step!\n" + }, + { + "db_id": "university_basketball", + "sql": "SELECT u.School, COUNT(b.Team_ID) AS Total_Teams\nFROM university u\nJOIN basketball_match b ON toString(u.School_ID) = toString(b.School_ID)\nWHERE u.Primary_conference LIKE 'ACC%'\nGROUP BY u.School\nHAVING COUNT(b.Team_ID) > 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "For ACC conference schools, find those with more than one basketball team and return the school names and their total team count.", + "external_knowledge": "", + "sql_candidate": [ + "SELECT u.School, COUNT(b.Team_ID) AS Total_Teams\nFROM university u\nJOIN basketball_match b ON toString(u.School_ID) = toString(b.School_ID)\nWHERE u.Primary_conference LIKE 'ACC%'\nGROUP BY u.School\nHAVING COUNT(b.Team_ID) > 1;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE basketball_match (\n `Team_ID` Nullable(Int64),\n `School_ID` Nullable(Int64),\n `Team_Name` Nullable(String),\n `ACC_Regular_Season` Nullable(String),\n `ACC_Percent` Nullable(String),\n `ACC_Home` Nullable(String),\n `ACC_Road` Nullable(String),\n `All_Games` Nullable(String),\n `All_Games_Percent` Nullable(Int64),\n `All_Home` Nullable(String),\n `All_Road` Nullable(String),\n `All_Neutral` Nullable(String),\n `basketball_match_description` Nullable(String)\n);\nCREATE TABLE university (\n `School_ID` Nullable(Int64),\n `School` Nullable(String),\n `Location` Nullable(String),\n `Founded` Nullable(Float64),\n `Affiliation` Nullable(String),\n `Enrollment` Nullable(Float64),\n `Nickname` Nullable(String),\n `Primary_conference` Nullable(String),\n `university_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE basketball_match (\n `Team_ID` Nullable(Int64),\n `School_ID` Nullable(Int64),\n `Team_Name` Nullable(String),\n `ACC_Regular_Season` Nullable(String),\n `ACC_Percent` Nullable(String),\n `ACC_Home` Nullable(String),\n `ACC_Road` Nullable(String),\n `All_Games` Nullable(String),\n `All_Games_Percent` Nullable(Int64),\n `All_Home` Nullable(String),\n `All_Road` Nullable(String),\n `All_Neutral` Nullable(String),\n `basketball_match_description` Nullable(String)\n);\nCREATE TABLE university (\n `School_ID` Nullable(Int64),\n `School` Nullable(String),\n `Location` Nullable(String),\n `Founded` Nullable(Float64),\n `Affiliation` Nullable(String),\n `Enrollment` Nullable(Float64),\n `Nickname` Nullable(String),\n `Primary_conference` Nullable(String),\n `university_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nFor ACC conference schools, find those with more than one basketball team and return the school names and their total team count.\n\nLet's think step by step!\n" + }, + { + "db_id": "culture_company", + "sql": "WITH BookClubMovies AS (\n SELECT \n cc.Company_name AS Company_name, \n bc.Book_Title AS Book_Title, \n m.Gross_worldwide AS Gross_worldwide\n FROM \n culture_company cc\n INNER JOIN \n book_club bc ON toString(cc.book_club_id) = toString(bc.book_club_id)\n INNER JOIN \n movie m ON toString(cc.movie_id) = toString(m.movie_id)\n)\nSELECT \n bc.Book_Title AS Book_Title, \n AVG(bcm.Gross_worldwide) AS Avg_Gross\nFROM \n BookClubMovies bcm\nINNER JOIN \n book_club bc ON toString(bcm.Book_Title) = toString(bc.Book_Title)\nGROUP BY \n bc.Book_Title;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please calculate the average worldwide gross revenue for each book title associated with any book club? I need to know this for all the movies related to book clubs!", + "external_knowledge": "", + "sql_candidate": [ + "WITH BookClubMovies AS (\n SELECT \n cc.Company_name AS Company_name, \n bc.Book_Title AS Book_Title, \n m.Gross_worldwide AS Gross_worldwide\n FROM \n culture_company cc\n INNER JOIN \n book_club bc ON toString(cc.book_club_id) = toString(bc.book_club_id)\n INNER JOIN \n movie m ON toString(cc.movie_id) = toString(m.movie_id)\n)\nSELECT \n bc.Book_Title AS Book_Title, \n AVG(bcm.Gross_worldwide) AS Avg_Gross\nFROM \n BookClubMovies bcm\nINNER JOIN \n book_club bc ON toString(bcm.Book_Title) = toString(bc.Book_Title)\nGROUP BY \n bc.Book_Title;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE book_club (\n `book_club_id` Nullable(Int64),\n `Year` Nullable(Int64),\n `Author_or_Editor` Nullable(String),\n `Book_Title` Nullable(String),\n `Publisher` Nullable(String),\n `Category` Nullable(String),\n `Result` Nullable(String),\n `book_club_description` Nullable(String)\n);\nCREATE TABLE culture_company (\n `Company_name` Nullable(String),\n `Type` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `book_club_id` Nullable(String),\n `movie_id` Nullable(String),\n `culture_company_description` Nullable(String)\n);\nCREATE TABLE movie (\n `movie_id` Nullable(Int64),\n `Title` Nullable(String),\n `Year` Nullable(Int64),\n `Director` Nullable(String),\n `Budget_million` Nullable(Float64),\n `Gross_worldwide` Nullable(Int64),\n `movie_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE book_club (\n `book_club_id` Nullable(Int64),\n `Year` Nullable(Int64),\n `Author_or_Editor` Nullable(String),\n `Book_Title` Nullable(String),\n `Publisher` Nullable(String),\n `Category` Nullable(String),\n `Result` Nullable(String),\n `book_club_description` Nullable(String)\n);\nCREATE TABLE culture_company (\n `Company_name` Nullable(String),\n `Type` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `book_club_id` Nullable(String),\n `movie_id` Nullable(String),\n `culture_company_description` Nullable(String)\n);\nCREATE TABLE movie (\n `movie_id` Nullable(Int64),\n `Title` Nullable(String),\n `Year` Nullable(Int64),\n `Director` Nullable(String),\n `Budget_million` Nullable(Float64),\n `Gross_worldwide` Nullable(Int64),\n `movie_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please calculate the average worldwide gross revenue for each book title associated with any book club? I need to know this for all the movies related to book clubs!\n\nLet's think step by step!\n" + }, + { + "db_id": "inn_1", + "sql": "SELECT r.roomName, SUM(res.Rate * r.basePrice) AS TotalRevenue\nFROM Rooms r\nJOIN Reservations res ON toString(r.RoomId) = toString(res.Room)\nGROUP BY r.roomName\nHAVING COUNT(res.Code) > 0;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please calculate the total revenue generated by each room and provide me with the room names and their corresponding total revenue amounts? Make sure to include only those rooms that have been booked at least once!", + "external_knowledge": "", + "sql_candidate": [ + "SELECT r.roomName, SUM(res.Rate * r.basePrice) AS TotalRevenue\nFROM Rooms r\nJOIN Reservations res ON toString(r.RoomId) = toString(res.Room)\nGROUP BY r.roomName\nHAVING COUNT(res.Code) > 0;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Reservations (\n `Code` Nullable(Int64),\n `Room` Nullable(String),\n `CheckIn` Nullable(String),\n `CheckOut` Nullable(String),\n `Rate` Nullable(Float64),\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Adults` Nullable(Int64),\n `Kids` Nullable(Int64),\n `Reservations_description` Nullable(String)\n);\nCREATE TABLE Rooms (\n `RoomId` Nullable(String),\n `roomName` Nullable(String),\n `beds` Nullable(Int64),\n `bedType` Nullable(String),\n `maxOccupancy` Nullable(Int64),\n `basePrice` Nullable(Int64),\n `decor` Nullable(String),\n `Rooms_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Reservations (\n `Code` Nullable(Int64),\n `Room` Nullable(String),\n `CheckIn` Nullable(String),\n `CheckOut` Nullable(String),\n `Rate` Nullable(Float64),\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Adults` Nullable(Int64),\n `Kids` Nullable(Int64),\n `Reservations_description` Nullable(String)\n);\nCREATE TABLE Rooms (\n `RoomId` Nullable(String),\n `roomName` Nullable(String),\n `beds` Nullable(Int64),\n `bedType` Nullable(String),\n `maxOccupancy` Nullable(Int64),\n `basePrice` Nullable(Int64),\n `decor` Nullable(String),\n `Rooms_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please calculate the total revenue generated by each room and provide me with the room names and their corresponding total revenue amounts? Make sure to include only those rooms that have been booked at least once!\n\nLet's think step by step!\n" + }, + { + "db_id": "driving_school", + "sql": "WITH StaffBornAfter1990 AS (\n SELECT staff_id\n FROM Staff\n WHERE date_of_birth > '1990-01-01'\n),\nLessonsWithIdentifiedStaff AS (\n SELECT l.customer_id, l.staff_id\n FROM Lessons l\n JOIN StaffBornAfter1990 sba\n ON toString(l.staff_id) = toString(sba.staff_id)\n),\nCustomerPaymentsSummary AS (\n SELECT cp.customer_id, SUM(cp.amount_payment) AS total_payment\n FROM Customer_Payments cp\n JOIN LessonsWithIdentifiedStaff lwis\n ON toString(cp.customer_id) = toString(lwis.customer_id)\n GROUP BY cp.customer_id\n)\nSELECT c.first_name, c.last_name, cps.total_payment\nFROM Customers c\nJOIN CustomerPaymentsSummary cps\nON toString(c.customer_id) = toString(cps.customer_id);", + "sql_result_column_count": 3, + "sql_result_rows_count": 6, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Could you help me find out the first names and last names of customers who took lessons from staff born after January 1, 1990, and also let me know the total amount they’ve paid for these lessons? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH StaffBornAfter1990 AS (\n SELECT staff_id\n FROM Staff\n WHERE date_of_birth > '1990-01-01'\n),\nLessonsWithIdentifiedStaff AS (\n SELECT l.customer_id, l.staff_id\n FROM Lessons l\n JOIN StaffBornAfter1990 sba\n ON toString(l.staff_id) = toString(sba.staff_id)\n),\nCustomerPaymentsSummary AS (\n SELECT cp.customer_id, SUM(cp.amount_payment) AS total_payment\n FROM Customer_Payments cp\n JOIN LessonsWithIdentifiedStaff lwis\n ON toString(cp.customer_id) = toString(lwis.customer_id)\n GROUP BY cp.customer_id\n)\nSELECT c.first_name, c.last_name, cps.total_payment\nFROM Customers c\nJOIN CustomerPaymentsSummary cps\nON toString(c.customer_id) = toString(cps.customer_id);" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String)\n);\nCREATE TABLE Customer_Payments (\n `customer_id` Int64,\n `datetime_payment` Date,\n `payment_method_code` String,\n `amount_payment` Nullable(Float64)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_address_id` Int64,\n `customer_status_code` String,\n `date_became_customer` Nullable(Date),\n `date_of_birth` Nullable(Date),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `amount_outstanding` Nullable(Float64),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `cell_mobile_phone_number` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Lessons (\n `lesson_id` Nullable(Int64),\n `customer_id` Int64,\n `lesson_status_code` String,\n `staff_id` Nullable(Int64),\n `vehicle_id` Int64,\n `lesson_date` Nullable(Date),\n `lesson_time` Nullable(String),\n `price` Nullable(Float64),\n `Lessons_description` Nullable(String)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_address_id` Int64,\n `nickname` Nullable(String),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `date_of_birth` Nullable(Date),\n `date_joined_staff` Nullable(Date),\n `date_left_staff` Nullable(Date),\n `Staff_description` Nullable(String)\n);\nCREATE TABLE Vehicles (\n `vehicle_id` Nullable(Int64),\n `vehicle_details` Nullable(String),\n `Vehicles_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String)\n);\nCREATE TABLE Customer_Payments (\n `customer_id` Int64,\n `datetime_payment` Date,\n `payment_method_code` String,\n `amount_payment` Nullable(Float64)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_address_id` Int64,\n `customer_status_code` String,\n `date_became_customer` Nullable(Date),\n `date_of_birth` Nullable(Date),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `amount_outstanding` Nullable(Float64),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `cell_mobile_phone_number` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Lessons (\n `lesson_id` Nullable(Int64),\n `customer_id` Int64,\n `lesson_status_code` String,\n `staff_id` Nullable(Int64),\n `vehicle_id` Int64,\n `lesson_date` Nullable(Date),\n `lesson_time` Nullable(String),\n `price` Nullable(Float64),\n `Lessons_description` Nullable(String)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_address_id` Int64,\n `nickname` Nullable(String),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `date_of_birth` Nullable(Date),\n `date_joined_staff` Nullable(Date),\n `date_left_staff` Nullable(Date),\n `Staff_description` Nullable(String)\n);\nCREATE TABLE Vehicles (\n `vehicle_id` Nullable(Int64),\n `vehicle_details` Nullable(String),\n `Vehicles_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you help me find out the first names and last names of customers who took lessons from staff born after January 1, 1990, and also let me know the total amount they’ve paid for these lessons? Thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "formula_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The 2015 Monaco Grand Prix was a thrilling race held in Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Monte Carlo Circuit, Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance\n FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance\n FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name\nFROM RaceCandidates r\nJOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId)\nORDER BY r.distance, c.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the top 5 race and circuit pairings that are closely tied to the thrilling 2015 Monaco Grand Prix in Monte Carlo and the Monte Carlo Circuit in Monaco? I want to see their names!", + "external_knowledge": "", + "sql_candidate": [ + "WITH RaceCandidates AS ( SELECT raceId, name, distance FROM races WHERE races_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Exciting 2015 Monaco F1 race in Monte Carlo.') AND k = 5 ), CircuitCandidates AS ( SELECT circuitId, name, distance FROM circuits WHERE circuits_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Monaco's Monte Carlo racing circuit') AND k = 5 ) SELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON r.raceId = c.circuitId ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '2015 Grand Prix in Monte Carlo, Monaco.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Famous Monte Carlo Circuit in Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thrilling 2015 Monaco GP at Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Monte Carlo Circuit in the heart of Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '2015 Monaco GP excitement in Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Iconic Monte Carlo Circuit, Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Monaco Grand Prix 2015 in Monte Carlo.') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Renowned Monte Carlo Circuit, Monaco') AS ref_vec_1,\n\nraces_filtered AS (\n SELECT\n *,\n distance(races_description_embedding, ref_vec_0) AS distance\n FROM races\n\n ORDER BY distance\n LIMIT 5\n),\n\ncircuits_filtered AS (\n SELECT\n *,\n distance(circuits_description_embedding, ref_vec_1) AS distance\n FROM circuits\n\n ORDER BY distance\n LIMIT 5\n),\n\nRaceCandidates AS (\n SELECT raceId, name, distance FROM races_filtered AS races\n),\n\nCircuitCandidates AS (\n SELECT circuitId, name, distance FROM circuits_filtered AS circuits\n)\n\nSELECT r.name AS race_name, c.name AS circuit_name FROM RaceCandidates r JOIN CircuitCandidates c ON toString(r.raceId) = toString(c.circuitId) ORDER BY r.distance, c.distance LIMIT 5;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE circuits (\n `circuitId` Nullable(Int64),\n `circuitRef` Nullable(String),\n `name` Nullable(String),\n `location` Nullable(String),\n `country` Nullable(String),\n `lat` Nullable(Float64),\n `lng` Nullable(Float64),\n `alt` Nullable(String),\n `url` Nullable(String),\n `circuits_description` Nullable(String),\n `circuits_description_embedding` Array(Float32)\n);\nCREATE TABLE constructorResults (\n `constructorResultsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `points` Nullable(Float64),\n `status` Nullable(String)\n);\nCREATE TABLE constructorStandings (\n `constructorStandingsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `points` Nullable(Float64),\n `position` Nullable(Int64),\n `positionText` Nullable(String),\n `wins` Nullable(Int64)\n);\nCREATE TABLE constructors (\n `constructorId` Nullable(Int64),\n `constructorRef` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `url` Nullable(String),\n `constructors_description` Nullable(String),\n `constructors_description_embedding` Array(Float32)\n);\nCREATE TABLE driverStandings (\n `driverStandingsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `points` Nullable(Float64),\n `position` Nullable(Int64),\n `positionText` Nullable(String),\n `wins` Nullable(Int64)\n);\nCREATE TABLE drivers (\n `driverId` Nullable(Int64),\n `driverRef` Nullable(String),\n `number` Nullable(String),\n `code` Nullable(String),\n `forename` Nullable(String),\n `surname` Nullable(String),\n `dob` Nullable(String),\n `nationality` Nullable(String),\n `url` Nullable(String),\n `drivers_description` Nullable(String),\n `drivers_description_embedding` Array(Float32)\n);\nCREATE TABLE lapTimes (\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `lap` Nullable(Int64),\n `position` Nullable(Int64),\n `time` Nullable(String),\n `milliseconds` Nullable(Int64)\n);\nCREATE TABLE pitStops (\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `stop` Nullable(Int64),\n `lap` Nullable(Int64),\n `time` Nullable(String),\n `duration` Nullable(String),\n `milliseconds` Nullable(Int64)\n);\nCREATE TABLE qualifying (\n `qualifyId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `number` Nullable(Int64),\n `position` Nullable(Int64),\n `q1` Nullable(String),\n `q2` Nullable(String),\n `q3` Nullable(String)\n);\nCREATE TABLE races (\n `raceId` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(Int64),\n `circuitId` Nullable(Int64),\n `name` Nullable(String),\n `date` Nullable(String),\n `time` Nullable(String),\n `url` Nullable(String),\n `races_description` Nullable(String),\n `races_description_embedding` Array(Float32)\n);\nCREATE TABLE results (\n `resultId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `number` Nullable(Int64),\n `grid` Nullable(Int64),\n `position` Nullable(String),\n `positionText` Nullable(String),\n `positionOrder` Nullable(Int64),\n `points` Nullable(Float64),\n `laps` Nullable(String),\n `time` Nullable(String),\n `milliseconds` Nullable(String),\n `fastestLap` Nullable(String),\n `rank` Nullable(String),\n `fastestLapTime` Nullable(String),\n `fastestLapSpeed` Nullable(String),\n `statusId` Nullable(Int64)\n);\nCREATE TABLE seasons (\n `year` Nullable(Int64),\n `url` Nullable(String),\n `seasons_description` Nullable(String),\n `seasons_description_embedding` Array(Float32)\n);\nCREATE TABLE status (\n `statusId` Nullable(Int64),\n `status` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE circuits (\n `circuitId` Nullable(Int64),\n `circuitRef` Nullable(String),\n `name` Nullable(String),\n `location` Nullable(String),\n `country` Nullable(String),\n `lat` Nullable(Float64),\n `lng` Nullable(Float64),\n `alt` Nullable(String),\n `url` Nullable(String),\n `circuits_description` Nullable(String),\n `circuits_description_embedding` Array(Float32)\n);\nCREATE TABLE constructorResults (\n `constructorResultsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `points` Nullable(Float64),\n `status` Nullable(String)\n);\nCREATE TABLE constructorStandings (\n `constructorStandingsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `points` Nullable(Float64),\n `position` Nullable(Int64),\n `positionText` Nullable(String),\n `wins` Nullable(Int64)\n);\nCREATE TABLE constructors (\n `constructorId` Nullable(Int64),\n `constructorRef` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `url` Nullable(String),\n `constructors_description` Nullable(String),\n `constructors_description_embedding` Array(Float32)\n);\nCREATE TABLE driverStandings (\n `driverStandingsId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `points` Nullable(Float64),\n `position` Nullable(Int64),\n `positionText` Nullable(String),\n `wins` Nullable(Int64)\n);\nCREATE TABLE drivers (\n `driverId` Nullable(Int64),\n `driverRef` Nullable(String),\n `number` Nullable(String),\n `code` Nullable(String),\n `forename` Nullable(String),\n `surname` Nullable(String),\n `dob` Nullable(String),\n `nationality` Nullable(String),\n `url` Nullable(String),\n `drivers_description` Nullable(String),\n `drivers_description_embedding` Array(Float32)\n);\nCREATE TABLE lapTimes (\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `lap` Nullable(Int64),\n `position` Nullable(Int64),\n `time` Nullable(String),\n `milliseconds` Nullable(Int64)\n);\nCREATE TABLE pitStops (\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `stop` Nullable(Int64),\n `lap` Nullable(Int64),\n `time` Nullable(String),\n `duration` Nullable(String),\n `milliseconds` Nullable(Int64)\n);\nCREATE TABLE qualifying (\n `qualifyId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `number` Nullable(Int64),\n `position` Nullable(Int64),\n `q1` Nullable(String),\n `q2` Nullable(String),\n `q3` Nullable(String)\n);\nCREATE TABLE races (\n `raceId` Nullable(Int64),\n `year` Nullable(Int64),\n `round` Nullable(Int64),\n `circuitId` Nullable(Int64),\n `name` Nullable(String),\n `date` Nullable(String),\n `time` Nullable(String),\n `url` Nullable(String),\n `races_description` Nullable(String),\n `races_description_embedding` Array(Float32)\n);\nCREATE TABLE results (\n `resultId` Nullable(Int64),\n `raceId` Nullable(Int64),\n `driverId` Nullable(Int64),\n `constructorId` Nullable(Int64),\n `number` Nullable(Int64),\n `grid` Nullable(Int64),\n `position` Nullable(String),\n `positionText` Nullable(String),\n `positionOrder` Nullable(Int64),\n `points` Nullable(Float64),\n `laps` Nullable(String),\n `time` Nullable(String),\n `milliseconds` Nullable(String),\n `fastestLap` Nullable(String),\n `rank` Nullable(String),\n `fastestLapTime` Nullable(String),\n `fastestLapSpeed` Nullable(String),\n `statusId` Nullable(Int64)\n);\nCREATE TABLE seasons (\n `year` Nullable(Int64),\n `url` Nullable(String),\n `seasons_description` Nullable(String),\n `seasons_description_embedding` Array(Float32)\n);\nCREATE TABLE status (\n `statusId` Nullable(Int64),\n `status` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you help me find the top 5 race and circuit pairings that are closely tied to the thrilling 2015 Monaco Grand Prix in Monte Carlo and the Monte Carlo Circuit in Monaco? I want to see their names!\n\nLet's think step by step!\n" + }, + { + "db_id": "city_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis with a large population and high GDP') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Please identify the top 5 cities that are characterized as bustling metropolises with large populations and high GDPs, and provide their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A large urban center with a significant population and strong economic performance') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Major city with high population density and substantial GDP') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A thriving city with a large populace and robust economy') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An economic hub with a vast population and high economic output') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A populous city with a dynamic economy and high GDP') AS ref_vec_0\n\nSELECT City_ID, City, distance(city.city_description_embedding, ref_vec_0) AS distance FROM city\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE city (\n `City_ID` Nullable(Int64),\n `City` Nullable(String),\n `Hanzi` Nullable(String),\n `Hanyu_Pinyin` Nullable(String),\n `Regional_Population` Nullable(Int64),\n `GDP` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE hosting_city (\n `Year` Nullable(Int64),\n `Match_ID` Nullable(Int64),\n `Host_City` Nullable(String)\n);\nCREATE TABLE match (\n `Match_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Score` Nullable(String),\n `Result` Nullable(String),\n `Competition` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE temperature (\n `City_ID` Nullable(Int64),\n `Jan` Nullable(Float64),\n `Feb` Nullable(Float64),\n `Mar` Nullable(Float64),\n `Apr` Nullable(Float64),\n `Jun` Nullable(Float64),\n `Jul` Nullable(Float64),\n `Aug` Nullable(Float64),\n `Sep` Nullable(Float64),\n `Oct` Nullable(Float64),\n `Nov` Nullable(Float64),\n `Dec` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE city (\n `City_ID` Nullable(Int64),\n `City` Nullable(String),\n `Hanzi` Nullable(String),\n `Hanyu_Pinyin` Nullable(String),\n `Regional_Population` Nullable(Int64),\n `GDP` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE hosting_city (\n `Year` Nullable(Int64),\n `Match_ID` Nullable(Int64),\n `Host_City` Nullable(String)\n);\nCREATE TABLE match (\n `Match_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Score` Nullable(String),\n `Result` Nullable(String),\n `Competition` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE temperature (\n `City_ID` Nullable(Int64),\n `Jan` Nullable(Float64),\n `Feb` Nullable(Float64),\n `Mar` Nullable(Float64),\n `Apr` Nullable(Float64),\n `Jun` Nullable(Float64),\n `Jul` Nullable(Float64),\n `Aug` Nullable(Float64),\n `Sep` Nullable(Float64),\n `Oct` Nullable(Float64),\n `Nov` Nullable(Float64),\n `Dec` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease identify the top 5 cities that are characterized as bustling metropolises with large populations and high GDPs, and provide their IDs and names.\n\nLet's think step by step!\n" + }, + { + "db_id": "city_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'metropolitan area with high population and economic activities') AS ref_vec_0,\n\nRankedCities AS (\n SELECT\n c.City_ID AS City_ID,\n c.City AS City,\n c.city_description AS city_description,\n distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM\n city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT\n h.Year AS Year,\n h.Match_ID AS Match_ID,\n h.Host_City AS Host_City\n FROM\n hosting_city h\n INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT\n rc.City AS City\nFROM\n RankedCities rc\nJOIN\n HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the grand tapestry of bustling cities, which urban centers resonate with the vibrancy of high population and economic activities, and have also hosted magnificent gatherings?", + "external_knowledge": "The SQL query employs vector operations to perform an approximate nearest neighbor (ANN) search using the `MATCH` operator. This search aims to identify the top 5 cities whose descriptions are most similar to the concept of a \"metropolitan area with high population and economic activities\", implying these are cities with significant population density and economic vibrancy. The `lembed('all-MiniLM-L6-v2', ...)` function utilizes embeddings to evaluate similarity based on Euclidean distance, with closer distances indicating higher similarity. This technique is useful for finding entities that conceptually align with specified criteria, such as identifying major urban centers in this context.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'urban centers with large populations and thriving economies') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'cities known for bustling economic activities and significant gatherings') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'major urban areas with vibrant populations and events') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'cities with significant population and economic vibrancy hosting events') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);", + "WITH\n lembed('all-MiniLM-L6-v2', 'urban areas bustling with population and economic activities hosting gatherings') AS ref_vec_0,\n\nRankedCities AS (\n SELECT c.City_ID, c.City, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nHostingMatches AS (\n SELECT h.Year, h.Match_ID, h.Host_City FROM hosting_city h INNER JOIN RankedCities rc ON toString(h.Host_City) = toString(rc.City_ID)\n)\n\nSELECT rc.City FROM RankedCities rc JOIN HostingMatches hm ON toString(rc.City_ID) = toString(hm.Host_City);" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE city (\n `City_ID` Nullable(Int64),\n `City` Nullable(String),\n `Hanzi` Nullable(String),\n `Hanyu_Pinyin` Nullable(String),\n `Regional_Population` Nullable(Int64),\n `GDP` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE hosting_city (\n `Year` Nullable(Int64),\n `Match_ID` Nullable(Int64),\n `Host_City` Nullable(String)\n);\nCREATE TABLE match (\n `Match_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Score` Nullable(String),\n `Result` Nullable(String),\n `Competition` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE temperature (\n `City_ID` Nullable(Int64),\n `Jan` Nullable(Float64),\n `Feb` Nullable(Float64),\n `Mar` Nullable(Float64),\n `Apr` Nullable(Float64),\n `Jun` Nullable(Float64),\n `Jul` Nullable(Float64),\n `Aug` Nullable(Float64),\n `Sep` Nullable(Float64),\n `Oct` Nullable(Float64),\n `Nov` Nullable(Float64),\n `Dec` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE city (\n `City_ID` Nullable(Int64),\n `City` Nullable(String),\n `Hanzi` Nullable(String),\n `Hanyu_Pinyin` Nullable(String),\n `Regional_Population` Nullable(Int64),\n `GDP` Nullable(Float64),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE hosting_city (\n `Year` Nullable(Int64),\n `Match_ID` Nullable(Int64),\n `Host_City` Nullable(String)\n);\nCREATE TABLE match (\n `Match_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Score` Nullable(String),\n `Result` Nullable(String),\n `Competition` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE temperature (\n `City_ID` Nullable(Int64),\n `Jan` Nullable(Float64),\n `Feb` Nullable(Float64),\n `Mar` Nullable(Float64),\n `Apr` Nullable(Float64),\n `Jun` Nullable(Float64),\n `Jul` Nullable(Float64),\n `Aug` Nullable(Float64),\n `Sep` Nullable(Float64),\n `Oct` Nullable(Float64),\n `Nov` Nullable(Float64),\n `Dec` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe SQL query employs vector operations to perform an approximate nearest neighbor (ANN) search using the `MATCH` operator. This search aims to identify the top 5 cities whose descriptions are most similar to the concept of a \"metropolitan area with high population and economic activities\", implying these are cities with significant population density and economic vibrancy. The `lembed('all-MiniLM-L6-v2', ...)` function utilizes embeddings to evaluate similarity based on Euclidean distance, with closer distances indicating higher similarity. This technique is useful for finding entities that conceptually align with specified criteria, such as identifying major urban centers in this context.\nIn the grand tapestry of bustling cities, which urban centers resonate with the vibrancy of high population and economic activities, and have also hosted magnificent gatherings?\n\nLet's think step by step!\n" + }, + { + "db_id": "aircraft", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Experienced pilot with international flying experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'International air race with high competition level') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance\n FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft \nFROM m_filtered AS m\nJOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name)\nORDER BY p.distance\nLIMIT 5;", + "sql_result_column_count": 8, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you identify a handful of air races in the US, where the winners are some of those really seasoned pilots with global flight experience, and tell me about the races, including the pilots and planes involved?", + "external_knowledge": "The SQL query employs vector operations to perform semantic searches using text embeddings. The `MATCH` operator in combination with `lembed()` finds records that are most similar to a given textual description by utilizing approximate nearest neighbor (ANN) search. The parameter `k` specifies the number of similar items to return, with the results being ranked by similarity. In this context, \"Experienced pilot with international flying experience\" and \"International air race with high competition level\" are the key descriptions guiding the semantic searches. The Euclidean distance is used as a metric for similarity, where a smaller distance indicates a closer match. This allows for flexible and nuanced retrieval based on conceptual similarity rather than exact keyword matching.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Veteran pilot with global flight credentials') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Competitive air race with seasoned participants') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pilot with extensive international aviation experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'High-level air race with experienced pilots') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Globally experienced pilot with vast flight history') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Internationally renowned air race with elite pilots') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pilot with significant global flight experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Prestigious air race featuring skilled pilots') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pilot with extensive global flying background') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Air race with top-tier pilots and international acclaim') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(match_description_embedding, ref_vec_1) AS distance\n FROM match\n WHERE match_description_embedding MATCH lembed('all-MiniLM-L6-v2', 'Air race with top-tier pilots AND international acclaim') AND Country = 'USA'\n ORDER BY distance\n LIMIT 5\n),\n\nNearestPilots AS (\n SELECT Pilot_Id, Name, Age, distance FROM pilot_filtered AS pilot\n)\n\nSELECT m.Round, m.Location, m.Country, m.Date, m.Fastest_Qualifying, m.Winning_Pilot, p.Name, m.Winning_Aircraft FROM m_filtered AS m JOIN NearestPilots p ON toString(m.Winning_Pilot) = toString(p.Name) ORDER BY p.distance LIMIT 5;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Aircraft` Nullable(String),\n `Description` Nullable(String),\n `Max_Gross_Weight` Nullable(String),\n `Total_disk_area` Nullable(String),\n `Max_disk_Loading` Nullable(String),\n `Description_embedding` Array(Float32)\n);\nCREATE TABLE aircraft_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE airport (\n `Airport_ID` Nullable(Int64),\n `Airport_Name` Nullable(String),\n `Total_Passengers` Nullable(Float64),\n `fld___Change_2007` Nullable(String),\n `International_Passengers` Nullable(Float64),\n `Domestic_Passengers` Nullable(Float64),\n `Transit_Passengers` Nullable(Float64),\n `Aircraft_Movements` Nullable(Float64),\n `Freight_Metric_Tonnes` Nullable(Float64),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE airport_aircraft (\n `ID` Nullable(Int64),\n `Airport_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64)\n);\nCREATE TABLE airport_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE match (\n `Round` Nullable(Float64),\n `Location` Nullable(String),\n `Country` Nullable(String),\n `Date` Nullable(String),\n `Fastest_Qualifying` Nullable(String),\n `Winning_Pilot` Nullable(String),\n `Winning_Aircraft` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE match_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE pilot (\n `Pilot_Id` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Aircraft` Nullable(String),\n `Description` Nullable(String),\n `Max_Gross_Weight` Nullable(String),\n `Total_disk_area` Nullable(String),\n `Max_disk_Loading` Nullable(String),\n `Description_embedding` Array(Float32)\n);\nCREATE TABLE aircraft_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE aircraft_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE airport (\n `Airport_ID` Nullable(Int64),\n `Airport_Name` Nullable(String),\n `Total_Passengers` Nullable(Float64),\n `fld___Change_2007` Nullable(String),\n `International_Passengers` Nullable(Float64),\n `Domestic_Passengers` Nullable(Float64),\n `Transit_Passengers` Nullable(Float64),\n `Aircraft_Movements` Nullable(Float64),\n `Freight_Metric_Tonnes` Nullable(Float64),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE airport_aircraft (\n `ID` Nullable(Int64),\n `Airport_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64)\n);\nCREATE TABLE airport_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE airport_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE match (\n `Round` Nullable(Float64),\n `Location` Nullable(String),\n `Country` Nullable(String),\n `Date` Nullable(String),\n `Fastest_Qualifying` Nullable(String),\n `Winning_Pilot` Nullable(String),\n `Winning_Aircraft` Nullable(String),\n `match_description` Nullable(String),\n `match_description_embedding` Array(Float32)\n);\nCREATE TABLE match_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE match_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE pilot (\n `Pilot_Id` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE pilot_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe SQL query employs vector operations to perform semantic searches using text embeddings. The `MATCH` operator in combination with `lembed()` finds records that are most similar to a given textual description by utilizing approximate nearest neighbor (ANN) search. The parameter `k` specifies the number of similar items to return, with the results being ranked by similarity. In this context, \"Experienced pilot with international flying experience\" and \"International air race with high competition level\" are the key descriptions guiding the semantic searches. The Euclidean distance is used as a metric for similarity, where a smaller distance indicates a closer match. This allows for flexible and nuanced retrieval based on conceptual similarity rather than exact keyword matching.\nCan you identify a handful of air races in the US, where the winners are some of those really seasoned pilots with global flight experience, and tell me about the races, including the pilots and planes involved?\n\nLet's think step by step!\n" + }, + { + "db_id": "e_government", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Contact details of John Doe who resides at 123 Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance\nFROM Individuals\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you identify the individual associated with the contact information for someone like John Doe living on Elm Street?", + "external_knowledge": "The `lembed` function generates an embedding vector from textual input using the `'all-MiniLM-L6-v2'` model. The `MATCH` operator conducts an approximate nearest neighbor search, which retrieves items based on vector similarity, typically using Euclidean distance. The similarity increases as the distance between vectors decreases. In this context, the operation aims to find individuals whose descriptions semantically relate to the idea of \"Contact details of John Doe who resides at 123 Elm Street,\" with the search constrained to return only one result.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the person linked to John Doe''''s contact info, who lives on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify the person associated with the contact details of John Doe on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Locate the individual connected to John Doe''''s contact information, residing on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for the person related to the contact info of John Doe who lives on Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Discover the individual tied to John Doe''''s contact details, living at Elm Street.') AS ref_vec_0\n\nSELECT individual_id, distance(Individuals.Individuals_description_embedding, ref_vec_0) AS distance FROM Individuals\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `town_city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Forms (\n `form_id` Nullable(Int64),\n `form_type_code` Nullable(String),\n `service_id` Nullable(Int64),\n `form_number` Nullable(String),\n `form_name` Nullable(String),\n `form_description` Nullable(String),\n `form_description_embedding` Array(Float32)\n);\nCREATE TABLE Individuals (\n `individual_id` Nullable(Int64),\n `individual_first_name` Nullable(String),\n `individual_middle_name` Nullable(String),\n `inidividual_phone` Nullable(String),\n `individual_email` Nullable(String),\n `individual_address` Nullable(String),\n `individual_last_name` Nullable(String),\n `Individuals_description` Nullable(String),\n `Individuals_description_embedding` Array(Float32)\n);\nCREATE TABLE Organization_Contact_Individuals (\n `individual_id` Int64,\n `organization_id` Int64,\n `date_contact_from` Date,\n `date_contact_to` Nullable(Date)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `date_formed` Nullable(String),\n `organization_name` Nullable(String),\n `uk_vat_number` Nullable(String),\n `Organizations_description` Nullable(String),\n `Organizations_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties (\n `party_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `party_phone` Nullable(String),\n `party_email` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Party_Addresses (\n `party_id` Int64,\n `address_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Party_Forms (\n `party_id` Int64,\n `form_id` Int64,\n `date_completion_started` Date,\n `form_status_code` String,\n `date_fully_completed` Nullable(Date)\n);\nCREATE TABLE Party_Services (\n `booking_id` Int64,\n `customer_id` Int64,\n `service_id` Int64,\n `service_datetime` Date,\n `booking_made_date` Nullable(Date)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `service_type_code` String,\n `service_name` Nullable(String),\n `service_descriptio` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `town_city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Forms (\n `form_id` Nullable(Int64),\n `form_type_code` Nullable(String),\n `service_id` Nullable(Int64),\n `form_number` Nullable(String),\n `form_name` Nullable(String),\n `form_description` Nullable(String),\n `form_description_embedding` Array(Float32)\n);\nCREATE TABLE Individuals (\n `individual_id` Nullable(Int64),\n `individual_first_name` Nullable(String),\n `individual_middle_name` Nullable(String),\n `inidividual_phone` Nullable(String),\n `individual_email` Nullable(String),\n `individual_address` Nullable(String),\n `individual_last_name` Nullable(String),\n `Individuals_description` Nullable(String),\n `Individuals_description_embedding` Array(Float32)\n);\nCREATE TABLE Organization_Contact_Individuals (\n `individual_id` Int64,\n `organization_id` Int64,\n `date_contact_from` Date,\n `date_contact_to` Nullable(Date)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `date_formed` Nullable(String),\n `organization_name` Nullable(String),\n `uk_vat_number` Nullable(String),\n `Organizations_description` Nullable(String),\n `Organizations_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties (\n `party_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `party_phone` Nullable(String),\n `party_email` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Party_Addresses (\n `party_id` Int64,\n `address_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Party_Forms (\n `party_id` Int64,\n `form_id` Int64,\n `date_completion_started` Date,\n `form_status_code` String,\n `date_fully_completed` Nullable(Date)\n);\nCREATE TABLE Party_Services (\n `booking_id` Int64,\n `customer_id` Int64,\n `service_id` Int64,\n `service_datetime` Date,\n `booking_made_date` Nullable(Date)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `service_type_code` String,\n `service_name` Nullable(String),\n `service_descriptio` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `lembed` function generates an embedding vector from textual input using the `'all-MiniLM-L6-v2'` model. The `MATCH` operator conducts an approximate nearest neighbor search, which retrieves items based on vector similarity, typically using Euclidean distance. The similarity increases as the distance between vectors decreases. In this context, the operation aims to find individuals whose descriptions semantically relate to the idea of \"Contact details of John Doe who resides at 123 Elm Street,\" with the search constrained to return only one result.\nCould you identify the individual associated with the contact information for someone like John Doe living on Elm Street?\n\nLet's think step by step!\n" + }, + { + "db_id": "imdb", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Great Adventure') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance\nFROM movie\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you fetch me the top 5 movies that have titles similar to \"The Great Adventure\"? I'd love to know their IDs and how close they are in terms of theme!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Epic Journey') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Grand Expedition') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Majestic Quest') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Incredible Voyage') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Great Exploration') AS ref_vec_0\n\nSELECT mid, distance(movie.title_embedding, ref_vec_0) AS distance FROM movie\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `aid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `actor_description` Nullable(String)\n);\nCREATE TABLE cast (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `aid` Nullable(Int64),\n `role` Nullable(Int64)\n);\nCREATE TABLE classification (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `gid` Nullable(Int64)\n);\nCREATE TABLE company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `country_code` Nullable(String),\n `company_description` Nullable(String)\n);\nCREATE TABLE copyright (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `cid` Nullable(Int64)\n);\nCREATE TABLE directed_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `did` Nullable(Int64)\n);\nCREATE TABLE director (\n `did` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `director_description` Nullable(String)\n);\nCREATE TABLE genre (\n `gid` Nullable(Int64),\n `genre` Nullable(String)\n);\nCREATE TABLE keyword (\n `id` Nullable(Int64),\n `keyword` Nullable(String)\n);\nCREATE TABLE made_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `pid` Nullable(Int64)\n);\nCREATE TABLE movie (\n `mid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `movie_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE producer (\n `pid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `producer_description` Nullable(String)\n);\nCREATE TABLE tags (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `kid` Nullable(Int64)\n);\nCREATE TABLE tv_series (\n `sid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `num_of_seasons` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `tv_series_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE writer (\n `wid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(Int64),\n `nationality` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `writer_description` Nullable(String)\n);\nCREATE TABLE written_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `wid` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE actor (\n `aid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `actor_description` Nullable(String)\n);\nCREATE TABLE cast (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `aid` Nullable(Int64),\n `role` Nullable(Int64)\n);\nCREATE TABLE classification (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `gid` Nullable(Int64)\n);\nCREATE TABLE company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `country_code` Nullable(String),\n `company_description` Nullable(String)\n);\nCREATE TABLE copyright (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `cid` Nullable(Int64)\n);\nCREATE TABLE directed_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `did` Nullable(Int64)\n);\nCREATE TABLE director (\n `did` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `director_description` Nullable(String)\n);\nCREATE TABLE genre (\n `gid` Nullable(Int64),\n `genre` Nullable(String)\n);\nCREATE TABLE keyword (\n `id` Nullable(Int64),\n `keyword` Nullable(String)\n);\nCREATE TABLE made_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `pid` Nullable(Int64)\n);\nCREATE TABLE movie (\n `mid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `movie_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE producer (\n `pid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(String),\n `nationality` Nullable(String),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `producer_description` Nullable(String)\n);\nCREATE TABLE tags (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `kid` Nullable(Int64)\n);\nCREATE TABLE tv_series (\n `sid` Nullable(Int64),\n `title` Nullable(String),\n `release_year` Nullable(Int64),\n `num_of_seasons` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `title_aka` Nullable(String),\n `budget` Nullable(String),\n `tv_series_description` Nullable(String),\n `title_embedding` Array(Float32),\n `title_aka_embedding` Array(Float32)\n);\nCREATE TABLE writer (\n `wid` Nullable(Int64),\n `gender` Nullable(String),\n `name` Nullable(Int64),\n `nationality` Nullable(Int64),\n `num_of_episodes` Nullable(Int64),\n `birth_city` Nullable(String),\n `birth_year` Nullable(Int64),\n `writer_description` Nullable(String)\n);\nCREATE TABLE written_by (\n `id` Nullable(Int64),\n `msid` Nullable(Int64),\n `wid` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey, can you fetch me the top 5 movies that have titles similar to \"The Great Adventure\"? I'd love to know their IDs and how close they are in terms of theme!\n\nLet's think step by step!\n" + }, + { + "db_id": "tracking_software_problems", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'User Interface issues and errors') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance\nFROM Problem_Category_Codes pcc\nJOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 15, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the descriptions of the top 3 problem categories related to user interface issues and errors along with their corresponding log entry descriptions?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'UI problems and errors') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'interface issues and error messages') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'user interface related problems') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'UI issues and error logs') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'problems with user interface and errors') AS ref_vec_0\n\nSELECT pcc.problem_category_description, pl.log_entry_description, distance(pcc.problem_category_description_embedding, ref_vec_0) AS distance FROM Problem_Category_Codes pcc JOIN Problem_Log pl ON toString(pcc.problem_category_code) = toString(pl.problem_category_code)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Problem_Category_Codes (\n `problem_category_code` Nullable(String),\n `problem_category_description` Nullable(String),\n `problem_category_description_embedding` Array(Float32)\n);\nCREATE TABLE Problem_Log (\n `problem_log_id` Nullable(Int64),\n `assigned_to_staff_id` Int64,\n `problem_id` Int64,\n `problem_category_code` String,\n `problem_status_code` String,\n `log_entry_date` Nullable(Date),\n `log_entry_description` Nullable(String),\n `log_entry_fix` Nullable(String),\n `other_log_details` Nullable(String)\n);\nCREATE TABLE Problem_Status_Codes (\n `problem_status_code` Nullable(String),\n `problem_status_description` Nullable(String)\n);\nCREATE TABLE Problems (\n `problem_id` Nullable(Int64),\n `product_id` Int64,\n `closure_authorised_by_staff_id` Int64,\n `reported_by_staff_id` Int64,\n `date_problem_reported` Date,\n `date_problem_closed` Nullable(Date),\n `problem_description` Nullable(String),\n `other_problem_details` Nullable(String)\n);\nCREATE TABLE Product (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_details` Nullable(String),\n `Product_description` Nullable(String),\n `Product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_first_name` Nullable(String),\n `staff_last_name` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the descriptions of the top 3 problem categories related to user interface issues and errors along with their corresponding log entry descriptions?\n\nLet's think step by step!\n" + }, + { + "db_id": "product_catalog", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3: ''''Tea Leaves'''' published by Green Tea Co. on March 15, 2015, last revised on September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance\nFROM Catalogs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the catalog name that best matches the description: \"Catalog ID 3: 'Tea Leaves' published by Green Tea Co. on March 15, 2015, last revised on September 12, 2019.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3 titled Tea Leaves by Green Tea Co., published on March 15, 2015, revised September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3: Tea Leaves, Green Tea Co. publisher, published March 15, 2015, last revised September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tea Leaves catalog by Green Tea Co., issued March 15, 2015, updated on September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Catalog ID 3, Tea Leaves from Green Tea Co., March 15, 2015 publication, revised September 12, 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tea Leaves catalog from Green Tea Co., published March 2015, last updated September 2019') AS ref_vec_0\n\nSELECT catalog_name, distance(Catalogs.Catalogs_description_embedding, ref_vec_0) AS distance FROM Catalogs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the catalog name that best matches the description: \"Catalog ID 3: 'Tea Leaves' published by Green Tea Co. on March 15, 2015, last revised on September 12, 2019.\"\n\nLet's think step by step!\n" + }, + { + "db_id": "pilot_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Experienced pilot from the USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom model equipped with diesel propulsion') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality\n FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain\n FROM aircraft_filtered AS aircraft\n)\n\nSELECT \n p.Pilot_name AS Pilot_name,\n p.Nationality AS Nationality,\n a.Manufacturer AS Manufacturer,\n a.Model AS Model,\n a.Powertrain AS Powertrain\nFROM FilteredPilots p\nJOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\nJOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID)\nORDER BY pr.Date DESC\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the names and nationalities of the top 3 experienced pilots from the USA and the manufacturers, models, and powertrains of the top 3 Gillig Phantom model aircraft equipped with diesel propulsion. Provide details for the most recent 5 pilot-aircraft pairings.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top skilled US pilots') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom diesel engine aircraft') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Veteran pilots from USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom with diesel propulsion system') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Experienced American pilots') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Diesel-powered Gillig Phantom aircraft') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly ranked pilots from USA') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom aircraft with diesel engines') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'USA pilots with extensive experience') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Gillig Phantom model with diesel power') AS ref_vec_1,\n\npilot_filtered AS (\n SELECT\n *,\n distance(pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot\n\n ORDER BY distance\n LIMIT 3\n),\n\naircraft_filtered AS (\n SELECT\n *,\n distance(aircraft_description_embedding, ref_vec_1) AS distance\n FROM aircraft\n\n ORDER BY distance\n LIMIT 3\n),\n\nFilteredPilots AS (\n SELECT Pilot_ID, Pilot_name, Rank, Age, Nationality FROM pilot_filtered AS pilot\n),\n\nFilteredAircraft AS (\n SELECT Aircraft_ID, Manufacturer, Model, Fleet_Series, Powertrain FROM aircraft_filtered AS aircraft\n)\n\nSELECT p.Pilot_name, p.Nationality, a.Manufacturer, a.Model, a.Powertrain FROM FilteredPilots p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID) JOIN FilteredAircraft a ON toString(pr.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY pr.Date DESC LIMIT 5;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Order_Year` Nullable(Int64),\n `Manufacturer` Nullable(String),\n `Model` Nullable(String),\n `Fleet_Series` Nullable(String),\n `Powertrain` Nullable(String),\n `Fuel_Propulsion` Nullable(String),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot (\n `Pilot_ID` Nullable(Int64),\n `Pilot_name` Nullable(String),\n `Rank` Nullable(Int64),\n `Age` Nullable(Int64),\n `Nationality` Nullable(String),\n `Position` Nullable(String),\n `Join_Year` Nullable(Int64),\n `Team` Nullable(String),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_record (\n `Record_ID` Nullable(Int64),\n `Pilot_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64),\n `Date` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Order_Year` Nullable(Int64),\n `Manufacturer` Nullable(String),\n `Model` Nullable(String),\n `Fleet_Series` Nullable(String),\n `Powertrain` Nullable(String),\n `Fuel_Propulsion` Nullable(String),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot (\n `Pilot_ID` Nullable(Int64),\n `Pilot_name` Nullable(String),\n `Rank` Nullable(Int64),\n `Age` Nullable(Int64),\n `Nationality` Nullable(String),\n `Position` Nullable(String),\n `Join_Year` Nullable(Int64),\n `Team` Nullable(String),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_record (\n `Record_ID` Nullable(Int64),\n `Pilot_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64),\n `Date` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the names and nationalities of the top 3 experienced pilots from the USA and the manufacturers, models, and powertrains of the top 3 Gillig Phantom model aircraft equipped with diesel propulsion. Provide details for the most recent 5 pilot-aircraft pairings.\n\nLet's think step by step!\n" + }, + { + "db_id": "musical", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A captivating musical journey with inspiring themes of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance\nFROM musical\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Can you identify the musical that most closely aligns with the themes of hope and redemption, described as a captivating journey?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A musical journey filled with themes of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An inspiring musical that explores hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A musical tale of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A journey through themes of hope and redemption in a musical') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A musical story highlighting themes of hope and redemption') AS ref_vec_0\n\nSELECT Musical_ID, distance(musical.musical_description_embedding, ref_vec_0) AS distance FROM musical\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `Actor_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Musical_ID` Nullable(Int64),\n `Character` Nullable(String),\n `Duration` Nullable(String),\n `age` Nullable(Int64),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE actor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE musical (\n `Musical_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Award` Nullable(String),\n `Category` Nullable(String),\n `Nominee` Nullable(String),\n `Result` Nullable(String),\n `musical_description` Nullable(String),\n `Category_embedding` Array(Float32),\n `musical_description_embedding` Array(Float32)\n);\nCREATE TABLE musical_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE musical_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE actor (\n `Actor_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Musical_ID` Nullable(Int64),\n `Character` Nullable(String),\n `Duration` Nullable(String),\n `age` Nullable(Int64),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE actor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE actor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE musical (\n `Musical_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Award` Nullable(String),\n `Category` Nullable(String),\n `Nominee` Nullable(String),\n `Result` Nullable(String),\n `musical_description` Nullable(String),\n `Category_embedding` Array(Float32),\n `musical_description_embedding` Array(Float32)\n);\nCREATE TABLE musical_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE musical_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE musical_vector_chunks01 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCan you identify the musical that most closely aligns with the themes of hope and redemption, described as a captivating journey?\n\nLet's think step by step!\n" + }, + { + "db_id": "pilot_record", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'experienced pilot from the US in a leadership role') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance\n FROM pilot p\n JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer\nFROM FilteredPilots fp\nJOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID)\nORDER BY fp.distance LIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the names and associated aircraft manufacturers of the top two pilots who best fit the profile of an experienced pilot from the US holding a leadership role.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'veteran US pilot with leadership experience') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'seasoned American pilot in a senior position') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'experienced US pilot with managerial duties') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'American pilot with extensive experience and leadership role') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'US pilot with significant experience in leadership') AS ref_vec_0,\n\nFilteredPilots AS (\n SELECT p.Pilot_ID, p.Pilot_name, pr.Aircraft_ID, distance(p.pilot_description_embedding, ref_vec_0) AS distance FROM pilot p JOIN pilot_record pr ON toString(p.Pilot_ID) = toString(pr.Pilot_ID)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fp.Pilot_name, a.Manufacturer FROM FilteredPilots fp JOIN aircraft a ON toString(fp.Aircraft_ID) = toString(a.Aircraft_ID) ORDER BY fp.distance LIMIT 2;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Order_Year` Nullable(Int64),\n `Manufacturer` Nullable(String),\n `Model` Nullable(String),\n `Fleet_Series` Nullable(String),\n `Powertrain` Nullable(String),\n `Fuel_Propulsion` Nullable(String),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot (\n `Pilot_ID` Nullable(Int64),\n `Pilot_name` Nullable(String),\n `Rank` Nullable(Int64),\n `Age` Nullable(Int64),\n `Nationality` Nullable(String),\n `Position` Nullable(String),\n `Join_Year` Nullable(Int64),\n `Team` Nullable(String),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_record (\n `Record_ID` Nullable(Int64),\n `Pilot_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64),\n `Date` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE aircraft (\n `Aircraft_ID` Nullable(Int64),\n `Order_Year` Nullable(Int64),\n `Manufacturer` Nullable(String),\n `Model` Nullable(String),\n `Fleet_Series` Nullable(String),\n `Powertrain` Nullable(String),\n `Fuel_Propulsion` Nullable(String),\n `aircraft_description` Nullable(String),\n `aircraft_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot (\n `Pilot_ID` Nullable(Int64),\n `Pilot_name` Nullable(String),\n `Rank` Nullable(Int64),\n `Age` Nullable(Int64),\n `Nationality` Nullable(String),\n `Position` Nullable(String),\n `Join_Year` Nullable(Int64),\n `Team` Nullable(String),\n `pilot_description` Nullable(String),\n `pilot_description_embedding` Array(Float32)\n);\nCREATE TABLE pilot_record (\n `Record_ID` Nullable(Int64),\n `Pilot_ID` Nullable(Int64),\n `Aircraft_ID` Nullable(Int64),\n `Date` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the names and associated aircraft manufacturers of the top two pilots who best fit the profile of an experienced pilot from the US holding a leadership role.\n\nLet's think step by step!\n" + }, + { + "db_id": "e_government", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Central Park in New York City, known for its vast green spaces, located in the USA') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance \nFROM Addresses\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the address ID for the location most similar to Central Park in New York City.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Famous urban park in New York City, USA, known for its green areas and recreational spaces') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Large public park located in NYC, celebrated for its nature and open spaces') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Iconic park in Manhattan, New York, recognized for its expansive greenery and attractions') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Central Park, a notable green space in New York City, offering vast landscapes and leisure activities') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned park in NYC, USA, featuring extensive gardens and recreational areas') AS ref_vec_0\n\nSELECT address_id, distance(Addresses.Addresses_description_embedding, ref_vec_0) AS distance FROM Addresses\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `town_city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Forms (\n `form_id` Nullable(Int64),\n `form_type_code` Nullable(String),\n `service_id` Nullable(Int64),\n `form_number` Nullable(String),\n `form_name` Nullable(String),\n `form_description` Nullable(String),\n `form_description_embedding` Array(Float32)\n);\nCREATE TABLE Individuals (\n `individual_id` Nullable(Int64),\n `individual_first_name` Nullable(String),\n `individual_middle_name` Nullable(String),\n `inidividual_phone` Nullable(String),\n `individual_email` Nullable(String),\n `individual_address` Nullable(String),\n `individual_last_name` Nullable(String),\n `Individuals_description` Nullable(String),\n `Individuals_description_embedding` Array(Float32)\n);\nCREATE TABLE Organization_Contact_Individuals (\n `individual_id` Int64,\n `organization_id` Int64,\n `date_contact_from` Date,\n `date_contact_to` Nullable(Date)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `date_formed` Nullable(String),\n `organization_name` Nullable(String),\n `uk_vat_number` Nullable(String),\n `Organizations_description` Nullable(String),\n `Organizations_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties (\n `party_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `party_phone` Nullable(String),\n `party_email` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Party_Addresses (\n `party_id` Int64,\n `address_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Party_Forms (\n `party_id` Int64,\n `form_id` Int64,\n `date_completion_started` Date,\n `form_status_code` String,\n `date_fully_completed` Nullable(Date)\n);\nCREATE TABLE Party_Services (\n `booking_id` Int64,\n `customer_id` Int64,\n `service_id` Int64,\n `service_datetime` Date,\n `booking_made_date` Nullable(Date)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `service_type_code` String,\n `service_name` Nullable(String),\n `service_descriptio` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1_number_building` Nullable(String),\n `town_city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `Addresses_description_embedding` Array(Float32)\n);\nCREATE TABLE Forms (\n `form_id` Nullable(Int64),\n `form_type_code` Nullable(String),\n `service_id` Nullable(Int64),\n `form_number` Nullable(String),\n `form_name` Nullable(String),\n `form_description` Nullable(String),\n `form_description_embedding` Array(Float32)\n);\nCREATE TABLE Individuals (\n `individual_id` Nullable(Int64),\n `individual_first_name` Nullable(String),\n `individual_middle_name` Nullable(String),\n `inidividual_phone` Nullable(String),\n `individual_email` Nullable(String),\n `individual_address` Nullable(String),\n `individual_last_name` Nullable(String),\n `Individuals_description` Nullable(String),\n `Individuals_description_embedding` Array(Float32)\n);\nCREATE TABLE Organization_Contact_Individuals (\n `individual_id` Int64,\n `organization_id` Int64,\n `date_contact_from` Date,\n `date_contact_to` Nullable(Date)\n);\nCREATE TABLE Organizations (\n `organization_id` Nullable(Int64),\n `date_formed` Nullable(String),\n `organization_name` Nullable(String),\n `uk_vat_number` Nullable(String),\n `Organizations_description` Nullable(String),\n `Organizations_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties (\n `party_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `party_phone` Nullable(String),\n `party_email` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Party_Addresses (\n `party_id` Int64,\n `address_id` Int64,\n `date_address_from` Date,\n `address_type_code` String,\n `date_address_to` Nullable(Date)\n);\nCREATE TABLE Party_Forms (\n `party_id` Int64,\n `form_id` Int64,\n `date_completion_started` Date,\n `form_status_code` String,\n `date_fully_completed` Nullable(Date)\n);\nCREATE TABLE Party_Services (\n `booking_id` Int64,\n `customer_id` Int64,\n `service_id` Int64,\n `service_datetime` Date,\n `booking_made_date` Nullable(Date)\n);\nCREATE TABLE Services (\n `service_id` Nullable(Int64),\n `service_type_code` String,\n `service_name` Nullable(String),\n `service_descriptio` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nFind the address ID for the location most similar to Central Park in New York City.\n\nLet's think step by step!\n" + }, + { + "db_id": "product_catalog", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high-quality chocolate bar with rich flavor and smooth texture.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT \n catalog_entry_id, \n price_in_dollars,\n distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance\n FROM \n Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n AVG(price_in_dollars) AS average_price_in_dollars\nFROM \n SimilarCatalogs;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "What is the average price of a selection of chocolate bars that are really similar to a top-notch one with a delightful taste and smooth feel?", + "external_knowledge": "In vector searches using the `sqlite-lembed` extension, the `MATCH` operator performs an approximate nearest neighbor (ANN) search to find items similar to a given query vector. The parameter `k=5` specifies that the query should return the top 5 items that are most similar to the query vector. This similarity is usually determined by calculating the Euclidean distance (L2 norm) between vectors, where a smaller distance indicates higher similarity. In this context, the query seeks to find chocolate bars that are most similar to the description of a \"high-quality chocolate bar with rich flavor and smooth texture.\" The description is converted into an embedding using the `lembed` function with the 'all-MiniLM-L6-v2' model, which represents semantic meanings in a high-dimensional space.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A premium chocolate bar with exquisite taste and velvety texture.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A luxurious chocolate bar known for its delightful taste and smooth finish.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An elite chocolate bar with a rich flavor profile and silky texture.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-tier chocolate bar characterized by its delightful taste and creamy feel.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A gourmet chocolate bar with a luscious flavor and smooth mouthfeel.') AS ref_vec_0,\n\nSimilarCatalogs AS (\n SELECT catalog_entry_id, price_in_dollars, distance(Catalog_Contents.Catalog_Contents_description_embedding, ref_vec_0) AS distance FROM Catalog_Contents\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT AVG(price_in_dollars) AS average_price_in_dollars FROM SimilarCatalogs;" + ], + "integration_level": 4, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn vector searches using the `sqlite-lembed` extension, the `MATCH` operator performs an approximate nearest neighbor (ANN) search to find items similar to a given query vector. The parameter `k=5` specifies that the query should return the top 5 items that are most similar to the query vector. This similarity is usually determined by calculating the Euclidean distance (L2 norm) between vectors, where a smaller distance indicates higher similarity. In this context, the query seeks to find chocolate bars that are most similar to the description of a \"high-quality chocolate bar with rich flavor and smooth texture.\" The description is converted into an embedding using the `lembed` function with the 'all-MiniLM-L6-v2' model, which represents semantic meanings in a high-dimensional space.\nWhat is the average price of a selection of chocolate bars that are really similar to a top-notch one with a delightful taste and smooth feel?\n\nLet's think step by step!\n" + }, + { + "db_id": "shop_membership", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Branch located in London with historical significance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance\nFROM membership_register_branch mr\nJOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Please provide the IDs of members associated with the top 5 branches that are described as being located in London with historical significance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Branches in London with historical relevance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'London branches with historical importance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Historically significant branches in London') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Branches located in London known for historical significance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top branches in London with historical significance') AS ref_vec_0\n\nSELECT mr.Member_ID, distance(b.branch_description_embedding, ref_vec_0) AS distance FROM membership_register_branch mr JOIN branch b ON toString(mr.Branch_ID) = toString(b.Branch_ID)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE branch (\n `Branch_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Open_year` Nullable(String),\n `Address_road` Nullable(String),\n `City` Nullable(String),\n `membership_amount` Nullable(String),\n `branch_description` Nullable(String),\n `branch_description_embedding` Array(Float32)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Card_Number` Nullable(String),\n `Name` Nullable(String),\n `Hometown` Nullable(String),\n `Level` Nullable(Int64),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE membership_register_branch (\n `Member_ID` Nullable(Int64),\n `Branch_ID` Nullable(String),\n `Register_Year` Nullable(String)\n);\nCREATE TABLE purchase (\n `Member_ID` Nullable(Int64),\n `Branch_ID` Nullable(String),\n `Year` Nullable(String),\n `Total_pounds` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE branch (\n `Branch_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Open_year` Nullable(String),\n `Address_road` Nullable(String),\n `City` Nullable(String),\n `membership_amount` Nullable(String),\n `branch_description` Nullable(String),\n `branch_description_embedding` Array(Float32)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Card_Number` Nullable(String),\n `Name` Nullable(String),\n `Hometown` Nullable(String),\n `Level` Nullable(Int64),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE membership_register_branch (\n `Member_ID` Nullable(Int64),\n `Branch_ID` Nullable(String),\n `Register_Year` Nullable(String)\n);\nCREATE TABLE purchase (\n `Member_ID` Nullable(Int64),\n `Branch_ID` Nullable(String),\n `Year` Nullable(String),\n `Total_pounds` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease provide the IDs of members associated with the top 5 branches that are described as being located in London with historical significance.\n\nLet's think step by step!\n" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John Doe, male, born on January 1, 1990, lives at 123 Main St, Anytown, USA, earns $50,000 annually, supervised by SSN 123456789, works in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance\nFROM employee\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the Social Security Number of the employee whose profile is most similar to that of John Doe, who is a male, born on January 1, 1990, currently residing at 123 Main St, Anytown, USA, earns $50,000 annually, is supervised by SSN 123456789, and works in department 3?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the employee with a profile closest to John Doe, a male born on January 1, 1990, residing at 123 Main St, Anytown, USA, earning $50,000 annually, supervised by SSN 123456789, and working in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Search for the SSN of the employee most similar to John Doe, who is a male, born on 1990-01-01, lives at 123 Main St, Anytown, USA, has a salary of $50,000, is overseen by SSN 123456789, and belongs to department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify the employee whose profile is most like John Doe''''s, a male born on January 1, 1990, currently living at 123 Main St, Anytown, USA, with an annual income of $50,000, under the supervision of SSN 123456789, and assigned to department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Retrieve the Social Security Number of the employee with the most similar profile to John Doe, male, born on January 1, 1990, residing at 123 Main St, Anytown, USA, earning $50,000 per year, supervised by SSN 123456789, and working in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Get the SSN of the employee whose profile matches John Doe''''s: male, born January 1, 1990, lives at 123 Main St, Anytown, USA, earns $50,000 annually, supervised by SSN 123456789, and works in department 3.') AS ref_vec_0\n\nSELECT Ssn, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the Social Security Number of the employee whose profile is most similar to that of John Doe, who is a male, born on January 1, 1990, currently residing at 123 Main St, Anytown, USA, earns $50,000 annually, is supervised by SSN 123456789, and works in department 3?\n\nLet's think step by step!\n" + }, + { + "db_id": "protein_institute", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard in London is a renowned skyscraper, known for its stunning glass facade and iconic silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance \nFROM building\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you find the building that is most representative of the description \"The Shard in London is a renowned skyscraper, known for its stunning glass facade and iconic silhouette,\" and give me its ID and the similarity distance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard is a famous skyscraper in London, celebrated for its glass facade and distinctive silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Known for its stunning glass exterior and iconic shape, The Shard in London stands out as a remarkable skyscraper.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard in London is a notable skyscraper, recognized for its beautiful glass facade and unique silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'London''''s Shard is a renowned skyscraper, distinguished by its impressive glass facade and iconic outline.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Shard, a prominent skyscraper in London, is known for its striking glass facade and memorable silhouette.') AS ref_vec_0\n\nSELECT building_id, distance(building.building_description_embedding, ref_vec_0) AS distance FROM building\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Institution (\n `Institution_id` Nullable(String),\n `Institution` Nullable(String),\n `Location` Nullable(String),\n `Founded` Nullable(Float64),\n `Type` Nullable(String),\n `Enrollment` Nullable(Int64),\n `Team` Nullable(String),\n `Primary_Conference` Nullable(String),\n `building_id` Nullable(String),\n `Institution_description` Nullable(String),\n `Institution_description_embedding` Array(Float32)\n);\nCREATE TABLE Institution_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE building (\n `building_id` Nullable(String),\n `Name` Nullable(String),\n `Street_address` Nullable(String),\n `Years_as_tallest` Nullable(String),\n `Height_feet` Nullable(Int64),\n `Floors` Nullable(Int64),\n `building_description` Nullable(String),\n `building_description_embedding` Array(Float32)\n);\nCREATE TABLE building_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE protein (\n `common_name` Nullable(String),\n `protein_name` Nullable(String),\n `divergence_from_human_lineage` Nullable(Float64),\n `accession_number` Nullable(String),\n `sequence_length` Nullable(Float64),\n `sequence_identity_to_human_protein` Nullable(String),\n `Institution_id` Nullable(String),\n `protein_description` Nullable(String),\n `protein_description_embedding` Array(Float32)\n);\nCREATE TABLE protein_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Institution (\n `Institution_id` Nullable(String),\n `Institution` Nullable(String),\n `Location` Nullable(String),\n `Founded` Nullable(Float64),\n `Type` Nullable(String),\n `Enrollment` Nullable(Int64),\n `Team` Nullable(String),\n `Primary_Conference` Nullable(String),\n `building_id` Nullable(String),\n `Institution_description` Nullable(String),\n `Institution_description_embedding` Array(Float32)\n);\nCREATE TABLE Institution_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Institution_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE building (\n `building_id` Nullable(String),\n `Name` Nullable(String),\n `Street_address` Nullable(String),\n `Years_as_tallest` Nullable(String),\n `Height_feet` Nullable(Int64),\n `Floors` Nullable(Int64),\n `building_description` Nullable(String),\n `building_description_embedding` Array(Float32)\n);\nCREATE TABLE building_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE building_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE protein (\n `common_name` Nullable(String),\n `protein_name` Nullable(String),\n `divergence_from_human_lineage` Nullable(Float64),\n `accession_number` Nullable(String),\n `sequence_length` Nullable(Float64),\n `sequence_identity_to_human_protein` Nullable(String),\n `Institution_id` Nullable(String),\n `protein_description` Nullable(String),\n `protein_description_embedding` Array(Float32)\n);\nCREATE TABLE protein_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE protein_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you find the building that is most representative of the description \"The Shard in London is a renowned skyscraper, known for its stunning glass facade and iconic silhouette,\" and give me its ID and the similarity distance?\n\nLet's think step by step!\n" + }, + { + "db_id": "voter_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A student from New York majoring in computer science') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT\n StuID,\n Election_Cycle,\n President_Vote,\n Vice_President_Vote\n FROM\n Voting_record\n WHERE\n Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT\n StuID,\n LName,\n Fname,\n Major,\n Advisor,\n distance(Student.Student_description_embedding, ref_vec_0) AS distance\n FROM\n Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT\n SS.StuID AS StuID\nFROM\n StudentSearch SS\nJOIN\n RecentVotes RV\nON toString(SS.StuID) = toString(RV.StuID)\nORDER BY\n SS.distance AS distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the top student who recently registered to vote after January 1, 2023, and is majoring in computer science from New York? I need their ID based on semantic similarity using the \"all-MiniLM-L6-v2\" model!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A computer science student from New York who registered to vote recently') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York student studying computer science registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Computer science major from New York who recently registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Student majoring in computer science from New York who recently registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York computer science student who has recently registered to vote') AS ref_vec_0,\n\nRecentVotes AS (\n SELECT StuID, Election_Cycle, President_Vote, Vice_President_Vote FROM Voting_record WHERE Registration_Date > '2023-01-01'\n),\n\nStudentSearch AS (\n SELECT StuID, LName, Fname, Major, Advisor, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT SS.StuID FROM StudentSearch SS JOIN RecentVotes RV ON toString(SS.StuID) = toString(RV.StuID) ORDER BY SS.distance LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Voting_record (\n `StuID` Nullable(Int64),\n `Registration_Date` Nullable(String),\n `Election_Cycle` Nullable(String),\n `President_Vote` Nullable(Int64),\n `Vice_President_Vote` Nullable(Int64),\n `Secretary_Vote` Nullable(Int64),\n `Treasurer_Vote` Nullable(Int64),\n `Class_President_Vote` Nullable(Int64),\n `Class_Senator_Vote` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Voting_record (\n `StuID` Nullable(Int64),\n `Registration_Date` Nullable(String),\n `Election_Cycle` Nullable(String),\n `President_Vote` Nullable(Int64),\n `Vice_President_Vote` Nullable(Int64),\n `Secretary_Vote` Nullable(Int64),\n `Treasurer_Vote` Nullable(Int64),\n `Class_President_Vote` Nullable(Int64),\n `Class_Senator_Vote` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the top student who recently registered to vote after January 1, 2023, and is majoring in computer science from New York? I need their ID based on semantic similarity using the \"all-MiniLM-L6-v2\" model!\n\nLet's think step by step!\n" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative technology development') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT \n Pnumber, \n Pname, \n distance(project.project_description_embedding, ref_vec_0) AS distance\n FROM \n project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n e.Fname || ' ' || e.Lname AS EmployeeName,\n p.Pname AS ProjectName\nFROM \n works_on w\nJOIN \n ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber)\nJOIN \n employee e ON toString(w.Essn) = toString(e.Ssn)\nORDER BY \n p.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Could you provide the names of employees and the projects they are currently working on, specifically for the top 5 projects that are most related to \"Innovative technology development\"? Please ensure that the results are ordered by their similarity distance and limited to the top 10 entries.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge tech development') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced technology innovation') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Pioneering tech projects') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative R&D in technology') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tech innovation and development') AS ref_vec_0,\n\nProjectVectorSearch AS (\n SELECT Pnumber, Pname, distance(project.project_description_embedding, ref_vec_0) AS distance FROM project\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.Fname || ' ' || e.Lname AS EmployeeName, p.Pname AS ProjectName FROM works_on w JOIN ProjectVectorSearch p ON toString(w.Pno) = toString(p.Pnumber) JOIN employee e ON toString(w.Essn) = toString(e.Ssn) ORDER BY p.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you provide the names of employees and the projects they are currently working on, specifically for the top 5 projects that are most related to \"Innovative technology development\"? Please ensure that the results are ordered by their similarity distance and limited to the top 10 entries.\n\nLet's think step by step!\n" + }, + { + "db_id": "debate", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A representative from California District 5 who is a Democrat aged 40.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance\nFROM people\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the grand theater of democracy, who are the five performers that step into the shoes of a 40-year-old Democrat representing California District 5?", + "external_knowledge": "The `MATCH` operator in the context of vector operations performs an approximate nearest neighbor (ANN) search, which is a common technique used to find data points that are most similar to a given query vector. The `lembed` function processes the input text \"A representative from California District 5 who is a Democrat aged 40\" using the 'all-MiniLM-L6-v2' model to generate a vector representation. The \"k=5\" clause specifies that the query should return the five nearest matches based on Euclidean distance. Lower distances indicate higher similarity to the provided description.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A 40-year-old Democrat from California''''s 5th district.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'California District 5 Democrat, aged 40.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Democratic representative, age 40, from California District 5.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Aged 40, Democrat, representing California''''s 5th District.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '40-year-old Democrat in California''''s 5th district.') AS ref_vec_0\n\nSELECT People_ID, Name, Age, distance(people.people_description_embedding, ref_vec_0) AS distance FROM people\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE debate (\n `Debate_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Num_of_Audience` Nullable(Int64),\n `debate_description` Nullable(String),\n `debate_description_embedding` Array(Float32)\n);\nCREATE TABLE debate_people (\n `Debate_ID` Nullable(Int64),\n `Affirmative` Nullable(Int64),\n `Negative` Nullable(Int64),\n `If_Affirmative_Win` Nullable(String)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `District` Nullable(String),\n `Name` Nullable(String),\n `Party` Nullable(String),\n `Age` Nullable(Int64),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE debate (\n `Debate_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Venue` Nullable(String),\n `Num_of_Audience` Nullable(Int64),\n `debate_description` Nullable(String),\n `debate_description_embedding` Array(Float32)\n);\nCREATE TABLE debate_people (\n `Debate_ID` Nullable(Int64),\n `Affirmative` Nullable(Int64),\n `Negative` Nullable(Int64),\n `If_Affirmative_Win` Nullable(String)\n);\nCREATE TABLE people (\n `People_ID` Nullable(Int64),\n `District` Nullable(String),\n `Name` Nullable(String),\n `Party` Nullable(String),\n `Age` Nullable(Int64),\n `people_description` Nullable(String),\n `people_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in the context of vector operations performs an approximate nearest neighbor (ANN) search, which is a common technique used to find data points that are most similar to a given query vector. The `lembed` function processes the input text \"A representative from California District 5 who is a Democrat aged 40\" using the 'all-MiniLM-L6-v2' model to generate a vector representation. The \"k=5\" clause specifies that the query should return the five nearest matches based on Euclidean distance. Lower distances indicate higher similarity to the provided description.\nIn the grand theater of democracy, who are the five performers that step into the shoes of a 40-year-old Democrat representing California District 5?\n\nLet's think step by step!\n" + }, + { + "db_id": "company_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Marketing department, numbered 3, is managed by an individual with SSN 123456789, who began managing the department on February 10, 2010.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance\nFROM department\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the top 5 departments that are most relevant to a description about the Marketing department, including the department name and similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Explore the top 5 departments closely related to the Marketing division, focusing on department names and similarity measures.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Find the five departments most similar to the Marketing department, including their names and similarity scores.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Identify departments most aligned with Marketing, showing department names and similarity levels.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Determine which five departments are most associated with Marketing, including their names and similarity distances.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'List the top five departments that have the strongest connection to Marketing, with department names and similarity metrics.') AS ref_vec_0\n\nSELECT Dname, distance(department.department_description_embedding, ref_vec_0) AS distance FROM department\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE department (\n `Dname` Nullable(String),\n `Dnumber` Nullable(Int64),\n `Mgr_ssn` Nullable(Int64),\n `Mgr_start_date` Nullable(String),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dependent (\n `Essn` Nullable(Int64),\n `Dependent_name` Nullable(String),\n `Sex` Nullable(String),\n `Bdate` Nullable(String),\n `Relationship` Nullable(String),\n `dependent_description` Nullable(String),\n `dependent_description_embedding` Array(Float32)\n);\nCREATE TABLE dependent_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE dependent_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE dept_locations (\n `Dnumber` Nullable(Int64),\n `Dlocation` Nullable(String)\n);\nCREATE TABLE employee (\n `Fname` Nullable(String),\n `Minit` Nullable(String),\n `Lname` Nullable(String),\n `Ssn` Nullable(Int64),\n `Bdate` Nullable(String),\n `Address` Nullable(String),\n `Sex` Nullable(String),\n `Salary` Nullable(Int64),\n `Super_ssn` Nullable(Int64),\n `Dno` Nullable(Int64),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE employee_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE employee_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE project (\n `Pname` Nullable(String),\n `Pnumber` Nullable(Int64),\n `Plocation` Nullable(String),\n `Dnum` Nullable(Int64),\n `project_description` Nullable(String),\n `project_description_embedding` Array(Float32)\n);\nCREATE TABLE project_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE project_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE works_on (\n `Essn` Nullable(Int64),\n `Pno` Nullable(Int64),\n `Hours` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 5 departments that are most relevant to a description about the Marketing department, including the department name and similarity distance.\n\nLet's think step by step!\n" + }, + { + "db_id": "assets_maintenance", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit related to a critical fault repair') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance\nFROM Engineer_Visits ev\nJOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the IDs of the top 5 engineer visits that are most relevant to handling a critical fault repair?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit for urgent fault resolution') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Visit by engineer to address critical fault') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit focused on critical fault repair') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Handling critical fault during engineer visit') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Engineer visit to manage urgent fault repair') AS ref_vec_0\n\nSELECT ev.engineer_visit_id, distance(ev.Engineer_Visits_description_embedding, ref_vec_0) AS distance FROM Engineer_Visits ev JOIN Fault_Log fl ON toString(ev.fault_log_entry_id) = toString(fl.fault_log_entry_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Asset_Parts (\n `asset_id` Int64,\n `part_id` Int64\n);\nCREATE TABLE Assets (\n `asset_id` Nullable(Int64),\n `maintenance_contract_id` Nullable(Int64),\n `supplier_company_id` Nullable(Int64),\n `asset_details` Nullable(String),\n `asset_make` Nullable(String),\n `asset_model` Nullable(String),\n `asset_acquired_date` Nullable(String),\n `asset_disposed_date` Nullable(String),\n `other_asset_details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Engineer_Skills (\n `engineer_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Engineer_Visits (\n `engineer_visit_id` Nullable(Int64),\n `contact_staff_id` Nullable(Int64),\n `engineer_id` Nullable(Int64),\n `fault_log_entry_id` Nullable(Int64),\n `fault_status` Nullable(String),\n `visit_start_datetime` Nullable(String),\n `visit_end_datetime` Nullable(String),\n `other_visit_details` Nullable(String),\n `Engineer_Visits_description` Nullable(String),\n `Engineer_Visits_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log (\n `fault_log_entry_id` Nullable(Int64),\n `asset_id` Nullable(Int64),\n `recorded_by_staff_id` Nullable(Int64),\n `fault_log_entry_datetime` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log_Parts (\n `fault_log_entry_id` Int64,\n `part_fault_id` Int64,\n `fault_status` String\n);\nCREATE TABLE Maintenance_Contracts (\n `maintenance_contract_id` Nullable(Int64),\n `maintenance_contract_company_id` Nullable(Int64),\n `contract_start_date` Nullable(String),\n `contract_end_date` Nullable(String),\n `other_contract_details` Nullable(String),\n `Maintenance_Contracts_description` Nullable(String),\n `Maintenance_Contracts_description_embedding` Array(Float32)\n);\nCREATE TABLE Maintenance_Engineers (\n `engineer_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `other_details` Nullable(String),\n `Maintenance_Engineers_description` Nullable(String),\n `other_details_embedding` Array(Float32),\n `Maintenance_Engineers_description_embedding` Array(Float32)\n);\nCREATE TABLE Part_Faults (\n `part_fault_id` Nullable(Int64),\n `part_id` Nullable(Int64),\n `fault_short_name` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Parts (\n `part_id` Nullable(Int64),\n `part_name` Nullable(String),\n `chargeable_yn` Nullable(String),\n `chargeable_amount` Nullable(String),\n `other_part_details` Nullable(String),\n `Parts_description` Nullable(String),\n `Parts_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills (\n `skill_id` Nullable(Int64),\n `skill_code` Nullable(String),\n `skill_description` Nullable(String),\n `skill_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills_Required_To_Fix (\n `part_fault_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_name` Nullable(String),\n `gender` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `other_staff_details_embedding` Array(Float32),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Third_Party_Companies (\n `company_id` Nullable(Int64),\n `company_type` Nullable(String),\n `company_name` Nullable(String),\n `company_address` Nullable(String),\n `other_company_details` Nullable(String),\n `Third_Party_Companies_description` Nullable(String),\n `other_company_details_embedding` Array(Float32),\n `Third_Party_Companies_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Asset_Parts (\n `asset_id` Int64,\n `part_id` Int64\n);\nCREATE TABLE Assets (\n `asset_id` Nullable(Int64),\n `maintenance_contract_id` Nullable(Int64),\n `supplier_company_id` Nullable(Int64),\n `asset_details` Nullable(String),\n `asset_make` Nullable(String),\n `asset_model` Nullable(String),\n `asset_acquired_date` Nullable(String),\n `asset_disposed_date` Nullable(String),\n `other_asset_details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Engineer_Skills (\n `engineer_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Engineer_Visits (\n `engineer_visit_id` Nullable(Int64),\n `contact_staff_id` Nullable(Int64),\n `engineer_id` Nullable(Int64),\n `fault_log_entry_id` Nullable(Int64),\n `fault_status` Nullable(String),\n `visit_start_datetime` Nullable(String),\n `visit_end_datetime` Nullable(String),\n `other_visit_details` Nullable(String),\n `Engineer_Visits_description` Nullable(String),\n `Engineer_Visits_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log (\n `fault_log_entry_id` Nullable(Int64),\n `asset_id` Nullable(Int64),\n `recorded_by_staff_id` Nullable(Int64),\n `fault_log_entry_datetime` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Fault_Log_Parts (\n `fault_log_entry_id` Int64,\n `part_fault_id` Int64,\n `fault_status` String\n);\nCREATE TABLE Maintenance_Contracts (\n `maintenance_contract_id` Nullable(Int64),\n `maintenance_contract_company_id` Nullable(Int64),\n `contract_start_date` Nullable(String),\n `contract_end_date` Nullable(String),\n `other_contract_details` Nullable(String),\n `Maintenance_Contracts_description` Nullable(String),\n `Maintenance_Contracts_description_embedding` Array(Float32)\n);\nCREATE TABLE Maintenance_Engineers (\n `engineer_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `other_details` Nullable(String),\n `Maintenance_Engineers_description` Nullable(String),\n `other_details_embedding` Array(Float32),\n `Maintenance_Engineers_description_embedding` Array(Float32)\n);\nCREATE TABLE Part_Faults (\n `part_fault_id` Nullable(Int64),\n `part_id` Nullable(Int64),\n `fault_short_name` Nullable(String),\n `fault_description` Nullable(String),\n `other_fault_details` Nullable(String),\n `fault_description_embedding` Array(Float32)\n);\nCREATE TABLE Parts (\n `part_id` Nullable(Int64),\n `part_name` Nullable(String),\n `chargeable_yn` Nullable(String),\n `chargeable_amount` Nullable(String),\n `other_part_details` Nullable(String),\n `Parts_description` Nullable(String),\n `Parts_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills (\n `skill_id` Nullable(Int64),\n `skill_code` Nullable(String),\n `skill_description` Nullable(String),\n `skill_description_embedding` Array(Float32)\n);\nCREATE TABLE Skills_Required_To_Fix (\n `part_fault_id` Int64,\n `skill_id` Int64\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_name` Nullable(String),\n `gender` Nullable(String),\n `other_staff_details` Nullable(String),\n `Staff_description` Nullable(String),\n `other_staff_details_embedding` Array(Float32),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Third_Party_Companies (\n `company_id` Nullable(Int64),\n `company_type` Nullable(String),\n `company_name` Nullable(String),\n `company_address` Nullable(String),\n `other_company_details` Nullable(String),\n `Third_Party_Companies_description` Nullable(String),\n `other_company_details_embedding` Array(Float32),\n `Third_Party_Companies_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the IDs of the top 5 engineer visits that are most relevant to handling a critical fault repair?\n\nLet's think step by step!\n" + }, + { + "db_id": "college_3", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old student majoring in Computer Science from Springfield') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance\n FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID\nFROM Enrolled_in E\nJOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID)\nORDER BY S.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Which courses are linked to the top 5 students resembling a 20-year-old Computer Science major from Springfield? List up to 10 courses.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A 20-year-old Computer Science student from Springfield') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Computer Science major, 20 years old, Springfield native') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Springfield-based 20-year-old studying Computer Science') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A Computer Science student aged 20 from Springfield') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', '20-year-old Springfield student majoring in Computer Science') AS ref_vec_0,\n\nStudentSimilarity AS (\n SELECT StuID, distance(Student.Student_description_embedding, ref_vec_0) AS distance FROM Student\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT E.CID FROM Enrolled_in E JOIN StudentSimilarity S ON toString(E.StuID) = toString(S.StuID) ORDER BY S.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Course (\n `CID` Nullable(String),\n `CName` Nullable(String),\n `Credits` Nullable(Int64),\n `Instructor` Nullable(Int64),\n `Days` Nullable(String),\n `Hours` Nullable(String),\n `DNO` Nullable(Int64),\n `Course_description` Nullable(String),\n `Course_description_embedding` Array(Float32)\n);\nCREATE TABLE Department (\n `DNO` Nullable(Int64),\n `Division` Nullable(String),\n `DName` Nullable(String),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `DPhone` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Enrolled_in (\n `StuID` Nullable(Int64),\n `CID` Nullable(String),\n `Grade` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Gradeconversion (\n `lettergrade` Nullable(String),\n `gradepoint` Nullable(Float64)\n);\nCREATE TABLE Member_of (\n `FacID` Nullable(Int64),\n `DNO` Nullable(Int64),\n `Appt_Type` Nullable(String)\n);\nCREATE TABLE Minor_in (\n `StuID` Nullable(Int64),\n `DNO` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Course (\n `CID` Nullable(String),\n `CName` Nullable(String),\n `Credits` Nullable(Int64),\n `Instructor` Nullable(Int64),\n `Days` Nullable(String),\n `Hours` Nullable(String),\n `DNO` Nullable(Int64),\n `Course_description` Nullable(String),\n `Course_description_embedding` Array(Float32)\n);\nCREATE TABLE Department (\n `DNO` Nullable(Int64),\n `Division` Nullable(String),\n `DName` Nullable(String),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `DPhone` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Enrolled_in (\n `StuID` Nullable(Int64),\n `CID` Nullable(String),\n `Grade` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Gradeconversion (\n `lettergrade` Nullable(String),\n `gradepoint` Nullable(Float64)\n);\nCREATE TABLE Member_of (\n `FacID` Nullable(Int64),\n `DNO` Nullable(Int64),\n `Appt_Type` Nullable(String)\n);\nCREATE TABLE Minor_in (\n `StuID` Nullable(Int64),\n `DNO` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhich courses are linked to the top 5 students resembling a 20-year-old Computer Science major from Springfield? List up to 10 courses.\n\nLet's think step by step!\n" + }, + { + "db_id": "school_player", + "sql": "SELECT s.School\nFROM school s\nINNER JOIN school_details sd ON toString(s.School_ID) = toString(sd.School_ID)\nINNER JOIN school_performance sp ON toString(s.School_ID) = toString(sp.School_Id)\nWHERE s.Year_Entered_Competition IS NOT NULL\nGROUP BY s.School\nORDER BY MAX(s.Enrollment) DESC\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Which school has risen to be the giant in terms of enrollment among those that have taken the leap and entered the competition arena?", + "external_knowledge": "No vector operations are involved in this query, so external knowledge related to vector operations is not applicable.", + "sql_candidate": [ + "SELECT s.School\nFROM school s\nINNER JOIN school_details sd ON toString(s.School_ID) = toString(sd.School_ID)\nINNER JOIN school_performance sp ON toString(s.School_ID) = toString(sp.School_Id)\nWHERE s.Year_Entered_Competition IS NOT NULL\nGROUP BY s.School\nORDER BY MAX(s.Enrollment) DESC\nLIMIT 1;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Team` Nullable(String),\n `Age` Nullable(Int64),\n `Position` Nullable(String),\n `School_ID` Nullable(Int64),\n `player_description` Nullable(String)\n);\nCREATE TABLE school (\n `School_ID` Nullable(Int64),\n `School` Nullable(String),\n `Location` Nullable(String),\n `Enrollment` Nullable(Float64),\n `Founded` Nullable(Float64),\n `Denomination` Nullable(String),\n `Boys_or_Girls` Nullable(String),\n `Day_or_Boarding` Nullable(String),\n `Year_Entered_Competition` Nullable(Float64),\n `School_Colors` Nullable(String),\n `school_description` Nullable(String)\n);\nCREATE TABLE school_details (\n `School_ID` Nullable(Int64),\n `Nickname` Nullable(String),\n `Colors` Nullable(String),\n `League` Nullable(String),\n `Class` Nullable(String),\n `Division` Nullable(String),\n `school_details_description` Nullable(String)\n);\nCREATE TABLE school_performance (\n `School_Id` Nullable(Int64),\n `School_Year` Nullable(String),\n `Class_A` Nullable(String),\n `Class_AA` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Team` Nullable(String),\n `Age` Nullable(Int64),\n `Position` Nullable(String),\n `School_ID` Nullable(Int64),\n `player_description` Nullable(String)\n);\nCREATE TABLE school (\n `School_ID` Nullable(Int64),\n `School` Nullable(String),\n `Location` Nullable(String),\n `Enrollment` Nullable(Float64),\n `Founded` Nullable(Float64),\n `Denomination` Nullable(String),\n `Boys_or_Girls` Nullable(String),\n `Day_or_Boarding` Nullable(String),\n `Year_Entered_Competition` Nullable(Float64),\n `School_Colors` Nullable(String),\n `school_description` Nullable(String)\n);\nCREATE TABLE school_details (\n `School_ID` Nullable(Int64),\n `Nickname` Nullable(String),\n `Colors` Nullable(String),\n `League` Nullable(String),\n `Class` Nullable(String),\n `Division` Nullable(String),\n `school_details_description` Nullable(String)\n);\nCREATE TABLE school_performance (\n `School_Id` Nullable(Int64),\n `School_Year` Nullable(String),\n `Class_A` Nullable(String),\n `Class_AA` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nNo vector operations are involved in this query, so external knowledge related to vector operations is not applicable.\nWhich school has risen to be the giant in terms of enrollment among those that have taken the leap and entered the competition arena?\n\nLet's think step by step!\n" + }, + { + "db_id": "product_catalog", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'chocolate handmade store') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cola with 1 liter capacity') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name\n FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance\n FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name\nFROM Contents_CTE c\nJOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id)\nORDER BY c.distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please identify the two catalog entries that best match the description of a \"Cola with 1 liter capacity\" and are found within catalogs that resemble a \"chocolate handmade store\". List their IDs and names for me!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'artisan chocolate shop') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '1 liter Cola') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'handcrafted chocolate boutique') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'liter-sized Cola drink') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'chocolate artisan store') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cola bottle 1 liter') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'handmade chocolate outlet') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cola with liter capacity') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;", + "WITH\n lembed('all-MiniLM-L6-v2', 'chocolate craft store') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '1 liter capacity Cola') AS ref_vec_1,\n\nCatalogs_filtered AS (\n SELECT\n *,\n distance(Catalogs_description_embedding, ref_vec_0) AS distance\n FROM Catalogs\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalog_Contents_filtered AS (\n SELECT\n *,\n distance(Catalog_Contents_description_embedding, ref_vec_1) AS distance\n FROM Catalog_Contents\n\n ORDER BY distance\n LIMIT 5\n),\n\nCatalogs_CTE AS (\n SELECT catalog_id, catalog_name FROM Catalogs_filtered AS Catalogs\n),\n\nContents_CTE AS (\n SELECT catalog_entry_id, catalog_entry_name, price_in_dollars, distance FROM Catalog_Contents_filtered AS Catalog_Contents\n)\n\nSELECT c.catalog_entry_id, c.catalog_entry_name FROM Contents_CTE c JOIN Catalogs_CTE ca ON toString(ca.catalog_id) = toString(c.catalog_entry_id) ORDER BY c.distance LIMIT 2;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Attribute_Definitions (\n `attribute_id` Nullable(Int64),\n `attribute_name` Nullable(String),\n `attribute_data_type` Nullable(String)\n);\nCREATE TABLE Catalog_Contents (\n `catalog_entry_id` Nullable(Int64),\n `catalog_level_number` Nullable(Int64),\n `parent_entry_id` Nullable(Int64),\n `previous_entry_id` Nullable(Int64),\n `next_entry_id` Nullable(Int64),\n `catalog_entry_name` Nullable(String),\n `product_stock_number` Nullable(String),\n `price_in_dollars` Nullable(Float64),\n `price_in_euros` Nullable(Float64),\n `price_in_pounds` Nullable(Float64),\n `capacity` Nullable(String),\n `length` Nullable(String),\n `height` Nullable(String),\n `width` Nullable(String),\n `Catalog_Contents_description` Nullable(String),\n `Catalog_Contents_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Contents_Additional_Attributes (\n `catalog_entry_id` Int64,\n `catalog_level_number` Int64,\n `attribute_id` Int64,\n `attribute_value` String\n);\nCREATE TABLE Catalog_Contents_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatachunks14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext12 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext13 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_metadatatext14 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Contents_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalog_Structure (\n `catalog_level_number` Nullable(Int64),\n `catalog_id` Nullable(Int64),\n `catalog_level_name` Nullable(String),\n `Catalog_Structure_description` Nullable(String),\n `Catalog_Structure_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalog_Structure_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalog_Structure_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Catalogs (\n `catalog_id` Nullable(Int64),\n `catalog_name` Nullable(String),\n `catalog_publisher` Nullable(String),\n `date_of_publication` Nullable(String),\n `date_of_latest_revision` Nullable(String),\n `Catalogs_description` Nullable(String),\n `Catalogs_description_embedding` Array(Float32)\n);\nCREATE TABLE Catalogs_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Catalogs_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nPlease identify the two catalog entries that best match the description of a \"Cola with 1 liter capacity\" and are found within catalogs that resemble a \"chocolate handmade store\". List their IDs and names for me!\n\nLet's think step by step!\n" + }, + { + "db_id": "customer_deliveries", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'durable and eco-friendly materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance\nFROM Products\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the product ID of the top product made with durable and eco-friendly materials?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'high-quality sustainable materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'eco-friendly and robust materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'durable green materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'long-lasting and environmentally friendly materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'sustainable and resilient materials') AS ref_vec_0\n\nSELECT product_id, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Actual_Order_Products (\n `actual_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Actual_Orders (\n `actual_order_id` Nullable(Int64),\n `order_status_code` String,\n `regular_order_id` Int64,\n `actual_order_date` Nullable(Date)\n);\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `address_type` String,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method` String,\n `customer_name` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `date_became_customer` Nullable(Date),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Delivery_Route_Locations (\n `location_code` Nullable(String),\n `route_id` Int64,\n `location_address_id` Int64,\n `location_name` Nullable(String)\n);\nCREATE TABLE Delivery_Routes (\n `route_id` Nullable(Int64),\n `route_name` Nullable(String),\n `other_route_details` Nullable(String),\n `Delivery_Routes_description` Nullable(String),\n `other_route_details_embedding` Array(Float32)\n);\nCREATE TABLE Employees (\n `employee_id` Nullable(Int64),\n `employee_address_id` Int64,\n `employee_name` Nullable(String),\n `employee_phone` Nullable(String),\n `Employees_description` Nullable(String)\n);\nCREATE TABLE Order_Deliveries (\n `location_code` String,\n `actual_order_id` Int64,\n `delivery_status_code` String,\n `driver_employee_id` Int64,\n `truck_id` Int64,\n `delivery_date` Nullable(Date)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Regular_Order_Products (\n `regular_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Regular_Orders (\n `regular_order_id` Nullable(Int64),\n `distributer_id` Int64\n);\nCREATE TABLE Trucks (\n `truck_id` Nullable(Int64),\n `truck_licence_number` Nullable(String),\n `truck_details` Nullable(String),\n `Trucks_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Actual_Order_Products (\n `actual_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Actual_Orders (\n `actual_order_id` Nullable(Int64),\n `order_status_code` String,\n `regular_order_id` Int64,\n `actual_order_date` Nullable(Date)\n);\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `Addresses_description` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `address_type` String,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method` String,\n `customer_name` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `date_became_customer` Nullable(Date),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Delivery_Route_Locations (\n `location_code` Nullable(String),\n `route_id` Int64,\n `location_address_id` Int64,\n `location_name` Nullable(String)\n);\nCREATE TABLE Delivery_Routes (\n `route_id` Nullable(Int64),\n `route_name` Nullable(String),\n `other_route_details` Nullable(String),\n `Delivery_Routes_description` Nullable(String),\n `other_route_details_embedding` Array(Float32)\n);\nCREATE TABLE Employees (\n `employee_id` Nullable(Int64),\n `employee_address_id` Int64,\n `employee_name` Nullable(String),\n `employee_phone` Nullable(String),\n `Employees_description` Nullable(String)\n);\nCREATE TABLE Order_Deliveries (\n `location_code` String,\n `actual_order_id` Int64,\n `delivery_status_code` String,\n `driver_employee_id` Int64,\n `truck_id` Int64,\n `delivery_date` Nullable(Date)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description` Nullable(String),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Regular_Order_Products (\n `regular_order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Regular_Orders (\n `regular_order_id` Nullable(Int64),\n `distributer_id` Int64\n);\nCREATE TABLE Trucks (\n `truck_id` Nullable(Int64),\n `truck_licence_number` Nullable(String),\n `truck_details` Nullable(String),\n `Trucks_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you help me find the product ID of the top product made with durable and eco-friendly materials?\n\nLet's think step by step!\n" + }, + { + "db_id": "program_share", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A program originating from Beijing and launched in 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance\nFROM program\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, could you help me find the ID of the program that started in Beijing back in 2004? I'm looking for just the one that best fits this description.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Program initiated in Beijing in the year 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Beijing-based program that commenced in 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Program started in Beijing during 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '2004 launch of a program in Beijing.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A program that began in Beijing in 2004.') AS ref_vec_0\n\nSELECT Program_ID, distance(program.program_description_embedding, ref_vec_0) AS distance FROM program\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE broadcast (\n `Channel_ID` Nullable(Int64),\n `Program_ID` Nullable(Int64),\n `Time_of_day` Nullable(String)\n);\nCREATE TABLE broadcast_share (\n `Channel_ID` Nullable(Int64),\n `Program_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Share_in_percent` Nullable(Float64)\n);\nCREATE TABLE channel (\n `Channel_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Owner` Nullable(String),\n `Share_in_percent` Nullable(Float64),\n `Rating_in_percent` Nullable(Float64),\n `channel_description` Nullable(String),\n `channel_description_embedding` Array(Float32)\n);\nCREATE TABLE program (\n `Program_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Origin` Nullable(String),\n `Launch` Nullable(Float64),\n `Owner` Nullable(String),\n `program_description` Nullable(String),\n `program_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE broadcast (\n `Channel_ID` Nullable(Int64),\n `Program_ID` Nullable(Int64),\n `Time_of_day` Nullable(String)\n);\nCREATE TABLE broadcast_share (\n `Channel_ID` Nullable(Int64),\n `Program_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Share_in_percent` Nullable(Float64)\n);\nCREATE TABLE channel (\n `Channel_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Owner` Nullable(String),\n `Share_in_percent` Nullable(Float64),\n `Rating_in_percent` Nullable(Float64),\n `channel_description` Nullable(String),\n `channel_description_embedding` Array(Float32)\n);\nCREATE TABLE program (\n `Program_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Origin` Nullable(String),\n `Launch` Nullable(Float64),\n `Owner` Nullable(String),\n `program_description` Nullable(String),\n `program_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey, could you help me find the ID of the program that started in Beijing back in 2004? I'm looking for just the one that best fits this description.\n\nLet's think step by step!\n" + }, + { + "db_id": "college_3", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced data structures and algorithms') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance\n FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName\nFROM Enrolled_in e\nJOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID)\nJOIN Course c ON toString(e.CID) = toString(c.CID)\nJOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID)\nJOIN Department d ON toString(mi.DNO) = toString(d.DNO)\nWHERE d.DName LIKE '%Computer Science%'\nORDER BY cm.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the realm of academia, who are the learners weaving through the threads of \"Advanced data structures and algorithms,\" while concurrently dancing within the minor of Computer Science? Reveal their identities and the courses they embrace.", + "external_knowledge": "The `MATCH` operator with `lembed()` in SQLite performs a vector search that helps in identifying items most similar to a given concept, based on embeddings. In this context, \"Advanced data structures and algorithms\" refers to complex course topics involving efficient data organization and problem-solving techniques. The search utilizes embeddings to measure similarity, typically calculated with Euclidean distance, where lower values suggest higher similarity. The `k=5` parameter specifies that we are interested in the top 5 courses that align most closely with the advanced data structures and algorithms concept.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Complex data structures and algorithmic strategies') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced algorithms and data structure techniques') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Data structures and algorithmic complexity') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced computational structures and algorithms') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sophisticated data structures with algorithmic focus') AS ref_vec_0,\n\nCourseMatch AS (\n SELECT CID, distance(Course.Course_description_embedding, ref_vec_0) AS distance FROM Course\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.StuID, c.CName FROM Enrolled_in e JOIN CourseMatch cm ON toString(e.CID) = toString(cm.CID) JOIN Course c ON toString(e.CID) = toString(c.CID) JOIN Minor_in mi ON toString(e.StuID) = toString(mi.StuID) JOIN Department d ON toString(mi.DNO) = toString(d.DNO) WHERE d.DName LIKE '%Computer Science%' ORDER BY cm.distance LIMIT 10;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Course (\n `CID` Nullable(String),\n `CName` Nullable(String),\n `Credits` Nullable(Int64),\n `Instructor` Nullable(Int64),\n `Days` Nullable(String),\n `Hours` Nullable(String),\n `DNO` Nullable(Int64),\n `Course_description` Nullable(String),\n `Course_description_embedding` Array(Float32)\n);\nCREATE TABLE Department (\n `DNO` Nullable(Int64),\n `Division` Nullable(String),\n `DName` Nullable(String),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `DPhone` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Enrolled_in (\n `StuID` Nullable(Int64),\n `CID` Nullable(String),\n `Grade` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Gradeconversion (\n `lettergrade` Nullable(String),\n `gradepoint` Nullable(Float64)\n);\nCREATE TABLE Member_of (\n `FacID` Nullable(Int64),\n `DNO` Nullable(Int64),\n `Appt_Type` Nullable(String)\n);\nCREATE TABLE Minor_in (\n `StuID` Nullable(Int64),\n `DNO` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Course (\n `CID` Nullable(String),\n `CName` Nullable(String),\n `Credits` Nullable(Int64),\n `Instructor` Nullable(Int64),\n `Days` Nullable(String),\n `Hours` Nullable(String),\n `DNO` Nullable(Int64),\n `Course_description` Nullable(String),\n `Course_description_embedding` Array(Float32)\n);\nCREATE TABLE Department (\n `DNO` Nullable(Int64),\n `Division` Nullable(String),\n `DName` Nullable(String),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `DPhone` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Enrolled_in (\n `StuID` Nullable(Int64),\n `CID` Nullable(String),\n `Grade` Nullable(String)\n);\nCREATE TABLE Faculty (\n `FacID` Nullable(Int64),\n `Lname` Nullable(String),\n `Fname` Nullable(String),\n `Rank` Nullable(String),\n `Sex` Nullable(String),\n `Phone` Nullable(Int64),\n `Room` Nullable(String),\n `Building` Nullable(String),\n `Faculty_description` Nullable(String),\n `Faculty_description_embedding` Array(Float32)\n);\nCREATE TABLE Gradeconversion (\n `lettergrade` Nullable(String),\n `gradepoint` Nullable(Float64)\n);\nCREATE TABLE Member_of (\n `FacID` Nullable(Int64),\n `DNO` Nullable(Int64),\n `Appt_Type` Nullable(String)\n);\nCREATE TABLE Minor_in (\n `StuID` Nullable(Int64),\n `DNO` Nullable(Int64)\n);\nCREATE TABLE Student (\n `StuID` Nullable(Int64),\n `LName` Nullable(String),\n `Fname` Nullable(String),\n `Age` Nullable(Int64),\n `Sex` Nullable(String),\n `Major` Nullable(Int64),\n `Advisor` Nullable(Int64),\n `city_code` Nullable(String),\n `Student_description` Nullable(String),\n `Student_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator with `lembed()` in SQLite performs a vector search that helps in identifying items most similar to a given concept, based on embeddings. In this context, \"Advanced data structures and algorithms\" refers to complex course topics involving efficient data organization and problem-solving techniques. The search utilizes embeddings to measure similarity, typically calculated with Euclidean distance, where lower values suggest higher similarity. The `k=5` parameter specifies that we are interested in the top 5 courses that align most closely with the advanced data structures and algorithms concept.\nIn the realm of academia, who are the learners weaving through the threads of \"Advanced data structures and algorithms,\" while concurrently dancing within the minor of Computer Science? Reveal their identities and the courses they embrace.\n\nLet's think step by step!\n" + }, + { + "db_id": "theme_gallery", + "sql": "SELECT e.Theme, SUM(er.Attendance) AS Total_Attendance\nFROM exhibition e\nJOIN exhibition_record er ON toString(e.Exhibition_ID) = toString(er.Exhibition_ID)\nWHERE e.Year = 2022\nGROUP BY e.Theme\nHAVING SUM(er.Attendance) > 1000\nORDER BY Total_Attendance DESC;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Find the themes of exhibitions held in the year 2022 where the total attendance exceeded 1000, and return the themes along with their total attendance figures, ordered by attendance from highest to lowest.", + "external_knowledge": "", + "sql_candidate": [ + "SELECT e.Theme, SUM(er.Attendance) AS Total_Attendance\nFROM exhibition e\nJOIN exhibition_record er ON toString(e.Exhibition_ID) = toString(er.Exhibition_ID)\nWHERE e.Year = 2022\nGROUP BY e.Theme\nHAVING SUM(er.Attendance) > 1000\nORDER BY Total_Attendance DESC;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Year_Join` Nullable(Int64),\n `Age` Nullable(Int64),\n `artist_description` Nullable(String)\n);\nCREATE TABLE exhibition (\n `Exhibition_ID` Nullable(Int64),\n `Year` Nullable(Int64),\n `Theme` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `Ticket_Price` Nullable(Float64),\n `exhibition_description` Nullable(String)\n);\nCREATE TABLE exhibition_record (\n `Exhibition_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Attendance` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Year_Join` Nullable(Int64),\n `Age` Nullable(Int64),\n `artist_description` Nullable(String)\n);\nCREATE TABLE exhibition (\n `Exhibition_ID` Nullable(Int64),\n `Year` Nullable(Int64),\n `Theme` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `Ticket_Price` Nullable(Float64),\n `exhibition_description` Nullable(String)\n);\nCREATE TABLE exhibition_record (\n `Exhibition_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Attendance` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nFind the themes of exhibitions held in the year 2022 where the total attendance exceeded 1000, and return the themes along with their total attendance figures, ordered by attendance from highest to lowest.\n\nLet's think step by step!\n" + }, + { + "db_id": "railway", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The locomotive was originally built at Midland Railway Works and is known for its iconic wheel arrangement.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance\nFROM railway\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Unearth the railway whose narrative spins around the Midland Railway Works and its famed wheel tapestry.", + "external_knowledge": "The `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search to find vectors in a column that are closest to a given query vector. In this case, the query vector is created using the `lembed('all-MiniLM-L6-v2', ...)` function, which converts the specified text into a vector using the MiniLM language model. The results are ranked by similarity, which is determined by calculating the Euclidean distance (L2 norm) between the vectors; smaller distances indicate higher similarity. The `LIMIT 1` clause ensures that only the most similar railway description is returned. The search mechanism is designed to find semantically similar entries without explicitly relying on keyword matching.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The story of the train revolves around the Midland Railway Works and its renowned wheel design.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Famous for its wheel tapestry, this railway piece was crafted at the Midland Railway Works.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Midland Railway Works is central to the tale of this train, celebrated for its distinctive wheel configuration.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'This railway narrative highlights the Midland Railway Works and its legendary wheel arrangement.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Known for its famous wheel tapestry, this engine was constructed at the Midland Railway Works.') AS ref_vec_0\n\nSELECT Railway_ID, distance(railway.railway_description_embedding, ref_vec_0) AS distance FROM railway\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE manager (\n `Manager_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Working_year_starts` Nullable(String),\n `Age` Nullable(Int64),\n `Level` Nullable(Int64),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE railway (\n `Railway_ID` Nullable(Int64),\n `Railway` Nullable(String),\n `Builder` Nullable(String),\n `Built` Nullable(String),\n `Wheels` Nullable(String),\n `Location` Nullable(String),\n `ObjectNumber` Nullable(String),\n `railway_description` Nullable(String),\n `railway_description_embedding` Array(Float32)\n);\nCREATE TABLE railway_manage (\n `Railway_ID` Nullable(Int64),\n `Manager_ID` Nullable(Int64),\n `From_Year` Nullable(String)\n);\nCREATE TABLE train (\n `Train_ID` Nullable(Int64),\n `Train_Num` Nullable(String),\n `Name` Nullable(String),\n `From` Nullable(String),\n `Arrival` Nullable(String),\n `Railway_ID` Nullable(Int64),\n `train_description` Nullable(String),\n `train_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE manager (\n `Manager_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Working_year_starts` Nullable(String),\n `Age` Nullable(Int64),\n `Level` Nullable(Int64),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE railway (\n `Railway_ID` Nullable(Int64),\n `Railway` Nullable(String),\n `Builder` Nullable(String),\n `Built` Nullable(String),\n `Wheels` Nullable(String),\n `Location` Nullable(String),\n `ObjectNumber` Nullable(String),\n `railway_description` Nullable(String),\n `railway_description_embedding` Array(Float32)\n);\nCREATE TABLE railway_manage (\n `Railway_ID` Nullable(Int64),\n `Manager_ID` Nullable(Int64),\n `From_Year` Nullable(String)\n);\nCREATE TABLE train (\n `Train_ID` Nullable(Int64),\n `Train_Num` Nullable(String),\n `Name` Nullable(String),\n `From` Nullable(String),\n `Arrival` Nullable(String),\n `Railway_ID` Nullable(Int64),\n `train_description` Nullable(String),\n `train_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search to find vectors in a column that are closest to a given query vector. In this case, the query vector is created using the `lembed('all-MiniLM-L6-v2', ...)` function, which converts the specified text into a vector using the MiniLM language model. The results are ranked by similarity, which is determined by calculating the Euclidean distance (L2 norm) between the vectors; smaller distances indicate higher similarity. The `LIMIT 1` clause ensures that only the most similar railway description is returned. The search mechanism is designed to find semantically similar entries without explicitly relying on keyword matching.\nUnearth the railway whose narrative spins around the Midland Railway Works and its famed wheel tapestry.\n\nLet's think step by step!\n" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary player inducted with overwhelming support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance\nFROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 6, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top 5 legendary players who got inducted with tons of support? I'd love to know their player IDs, the year they were inducted, who voted for them, how many ballots and votes they got, and how closely they matched this legendary status!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top legendary players with significant induction support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary athletes inducted with high voter approval') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players inducted as legends with strong backing') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hall of Fame legends with substantial support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Legendary figures inducted with extensive voter support') AS ref_vec_0\n\nSELECT player_id, yearid, votedby, ballots, votes, distance(hall_of_fame.hall_of_fame_description_embedding, ref_vec_0) AS distance FROM hall_of_fame\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you find me the top 5 legendary players who got inducted with tons of support? I'd love to know their player IDs, the year they were inducted, who voted for them, how many ballots and votes they got, and how closely they matched this legendary status!\n\nLet's think step by step!\n" + }, + { + "db_id": "wine_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A 2010 Chardonnay from Sonoma County, priced at $25 with a score of 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance\nFROM wine\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need to find the name of the wine that best fits the description of a 2010 Chardonnay from Sonoma County, priced at $25 and scored at 90. Could you identify the top match?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Sonoma County 2010 Chardonnay, $25 price, 90 score.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Chardonnay from 2010 in Sonoma, $25 and rated 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', '2010 Sonoma Chardonnay, priced $25, score of 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best match for 2010 Chardonnay, Sonoma County, $25, score 90.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top 2010 Sonoma Chardonnay, $25, 90 points.') AS ref_vec_0\n\nSELECT Name, distance(wine.wine_description_embedding, ref_vec_0) AS distance FROM wine\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE appellations (\n `No` Nullable(Int64),\n `Appelation` Nullable(String),\n `County` Nullable(String),\n `State` Nullable(String),\n `Area` Nullable(String),\n `isAVA` Nullable(String),\n `appellations_description` Nullable(String),\n `appellations_description_embedding` Array(Float32)\n);\nCREATE TABLE grapes (\n `ID` Nullable(Int64),\n `Grape` Nullable(String),\n `Color` Nullable(String),\n `grapes_description` Nullable(String),\n `grapes_description_embedding` Array(Float32)\n);\nCREATE TABLE wine (\n `No` Nullable(Int64),\n `Grape` Nullable(String),\n `Winery` Nullable(String),\n `Appelation` Nullable(String),\n `State` Nullable(String),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Price` Nullable(Int64),\n `Score` Nullable(Int64),\n `Cases` Nullable(Int64),\n `Drink` Nullable(String),\n `wine_description` Nullable(String),\n `wine_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE appellations (\n `No` Nullable(Int64),\n `Appelation` Nullable(String),\n `County` Nullable(String),\n `State` Nullable(String),\n `Area` Nullable(String),\n `isAVA` Nullable(String),\n `appellations_description` Nullable(String),\n `appellations_description_embedding` Array(Float32)\n);\nCREATE TABLE grapes (\n `ID` Nullable(Int64),\n `Grape` Nullable(String),\n `Color` Nullable(String),\n `grapes_description` Nullable(String),\n `grapes_description_embedding` Array(Float32)\n);\nCREATE TABLE wine (\n `No` Nullable(Int64),\n `Grape` Nullable(String),\n `Winery` Nullable(String),\n `Appelation` Nullable(String),\n `State` Nullable(String),\n `Name` Nullable(String),\n `Year` Nullable(Int64),\n `Price` Nullable(Int64),\n `Score` Nullable(Int64),\n `Cases` Nullable(Int64),\n `Drink` Nullable(String),\n `wine_description` Nullable(String),\n `wine_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI need to find the name of the wine that best fits the description of a 2010 Chardonnay from Sonoma County, priced at $25 and scored at 90. Could you identify the top match?\n\nLet's think step by step!\n" + }, + { + "db_id": "products_gen_characteristics", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Herbal Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT \n p.product_id AS product_id,\n p.color_code AS color_code,\n p.product_category_code AS product_category_code,\n distance(p.product_description_embedding, ref_vec_0) AS distance\n FROM \n Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id\nFROM ProductSimilarity ps\nJOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code)\nJOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code)\nORDER BY ps.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the product ID for the top product that matches the concept of \"Herbal Tea\", considering the color and category details?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Natural Herbal Infusion') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Herbal Blend Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Botanical Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Tea with Herbal Notes') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Herbal Infused Tea') AS ref_vec_0,\n\nProductSimilarity AS (\n SELECT p.product_id, p.color_code, p.product_category_code, distance(p.product_description_embedding, ref_vec_0) AS distance FROM Products p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ps.product_id FROM ProductSimilarity ps JOIN Ref_Colors rc ON toString(ps.color_code) = toString(rc.color_code) JOIN Ref_Product_Categories rpc ON toString(ps.product_category_code) = toString(rpc.product_category_code) ORDER BY ps.distance LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Characteristics (\n `characteristic_id` Nullable(Int64),\n `characteristic_type_code` Nullable(String),\n `characteristic_data_type` Nullable(String),\n `characteristic_name` Nullable(String),\n `other_characteristic_details` Nullable(String),\n `Characteristics_description` Nullable(String),\n `other_characteristic_details_embedding` Array(Float32)\n);\nCREATE TABLE Product_Characteristics (\n `product_id` Int64,\n `characteristic_id` Int64,\n `product_characteristic_value` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `color_code` Nullable(String),\n `product_category_code` Nullable(String),\n `product_name` Nullable(String),\n `typical_buying_price` Nullable(String),\n `typical_selling_price` Nullable(String),\n `product_description` Nullable(String),\n `other_product_details` Nullable(String),\n `product_description_embedding` Array(Float32),\n `other_product_details_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Characteristic_Types (\n `characteristic_type_code` Nullable(String),\n `characteristic_type_description` Nullable(String),\n `characteristic_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Colors (\n `color_code` Nullable(String),\n `color_description` Nullable(String),\n `color_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Product_Categories (\n `product_category_code` Nullable(String),\n `product_category_description` Nullable(String),\n `unit_of_measure` Nullable(String),\n `product_category_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Characteristics (\n `characteristic_id` Nullable(Int64),\n `characteristic_type_code` Nullable(String),\n `characteristic_data_type` Nullable(String),\n `characteristic_name` Nullable(String),\n `other_characteristic_details` Nullable(String),\n `Characteristics_description` Nullable(String),\n `other_characteristic_details_embedding` Array(Float32)\n);\nCREATE TABLE Product_Characteristics (\n `product_id` Int64,\n `characteristic_id` Int64,\n `product_characteristic_value` Nullable(String)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `color_code` Nullable(String),\n `product_category_code` Nullable(String),\n `product_name` Nullable(String),\n `typical_buying_price` Nullable(String),\n `typical_selling_price` Nullable(String),\n `product_description` Nullable(String),\n `other_product_details` Nullable(String),\n `product_description_embedding` Array(Float32),\n `other_product_details_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Characteristic_Types (\n `characteristic_type_code` Nullable(String),\n `characteristic_type_description` Nullable(String),\n `characteristic_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Colors (\n `color_code` Nullable(String),\n `color_description` Nullable(String),\n `color_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Product_Categories (\n `product_category_code` Nullable(String),\n `product_category_description` Nullable(String),\n `unit_of_measure` Nullable(String),\n `product_category_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the product ID for the top product that matches the concept of \"Herbal Tea\", considering the color and category details?\n\nLet's think step by step!\n" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in the national league') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance\nFROM player_award pa\nJOIN player p ON toString(pa.player_id) = toString(p.player_id)\nWHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the top 3 players recognized for their outstanding performance in the national league, and provide their full names and award IDs, specifically including those players born in 1990 or originating from the United States.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top players in national league performance') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional national league achievements') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Recognized for national league excellence') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in national sports league') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top performers in the national league') AS ref_vec_0\n\nSELECT p.name_first || ' ' || p.name_last AS player_full_name, pa.award_id, distance(pa.notes_embedding, ref_vec_0) AS distance FROM player_award pa JOIN player p ON toString(pa.player_id) = toString(p.player_id) WHERE (p.birth_year = 1990 OR p.birth_country = 'United States')\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 3 players recognized for their outstanding performance in the national league, and provide their full names and award IDs, specifically including those players born in 1990 or originating from the United States.\n\nLet's think step by step!\n" + }, + { + "db_id": "real_estate_properties", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Amenity features include facilities such as pools that enhance comfort') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance\nFROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 feature IDs that relate to amenity features enhancing comfort, like having pools? I need them for an upcoming report!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Comfort-enhancing amenities like pools for relaxation') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Features that boost comfort with amenities such as swimming pools') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Amenities that enhance comfort, including pool facilities') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Comfortable living features like pools for leisure') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Facilities such as pools that improve comfort and relaxation') AS ref_vec_0\n\nSELECT feature_id, distance(Other_Available_Features.feature_description_embedding, ref_vec_0) AS distance FROM Other_Available_Features\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Other_Available_Features (\n `feature_id` Nullable(Int64),\n `feature_type_code` Nullable(String),\n `feature_name` Nullable(String),\n `feature_description` Nullable(String),\n `feature_description_embedding` Array(Float32)\n);\nCREATE TABLE Other_Available_Features_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Other_Property_Features (\n `property_id` Int64,\n `feature_id` Int64,\n `property_feature_description` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` String,\n `date_on_market` Nullable(Date),\n `date_sold` Nullable(Date),\n `property_name` Nullable(String),\n `property_address` Nullable(String),\n `room_count` Nullable(Int64),\n `vendor_requested_price` Nullable(Decimal(38, 6)),\n `buyer_offered_price` Nullable(Decimal(38, 6)),\n `agreed_selling_price` Nullable(Decimal(38, 6)),\n `apt_feature_1` Nullable(String),\n `apt_feature_2` Nullable(String),\n `apt_feature_3` Nullable(String),\n `fld_feature_1` Nullable(String),\n `fld_feature_2` Nullable(String),\n `fld_feature_3` Nullable(String),\n `hse_feature_1` Nullable(String),\n `hse_feature_2` Nullable(String),\n `hse_feature_3` Nullable(String),\n `oth_feature_1` Nullable(String),\n `oth_feature_2` Nullable(String),\n `oth_feature_3` Nullable(String),\n `shp_feature_1` Nullable(String),\n `shp_feature_2` Nullable(String),\n `shp_feature_3` Nullable(String),\n `other_property_details` Nullable(String),\n `Properties_description` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types (\n `feature_type_code` Nullable(String),\n `feature_type_name` Nullable(String),\n `Ref_Feature_Types_description` Nullable(String),\n `Ref_Feature_Types_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types (\n `property_type_code` Nullable(String),\n `property_type_description` Nullable(String),\n `property_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Property_Types_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Other_Available_Features (\n `feature_id` Nullable(Int64),\n `feature_type_code` Nullable(String),\n `feature_name` Nullable(String),\n `feature_description` Nullable(String),\n `feature_description_embedding` Array(Float32)\n);\nCREATE TABLE Other_Available_Features_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Other_Available_Features_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Other_Property_Features (\n `property_id` Int64,\n `feature_id` Int64,\n `property_feature_description` Nullable(String)\n);\nCREATE TABLE Properties (\n `property_id` Nullable(Int64),\n `property_type_code` String,\n `date_on_market` Nullable(Date),\n `date_sold` Nullable(Date),\n `property_name` Nullable(String),\n `property_address` Nullable(String),\n `room_count` Nullable(Int64),\n `vendor_requested_price` Nullable(Decimal(38, 6)),\n `buyer_offered_price` Nullable(Decimal(38, 6)),\n `agreed_selling_price` Nullable(Decimal(38, 6)),\n `apt_feature_1` Nullable(String),\n `apt_feature_2` Nullable(String),\n `apt_feature_3` Nullable(String),\n `fld_feature_1` Nullable(String),\n `fld_feature_2` Nullable(String),\n `fld_feature_3` Nullable(String),\n `hse_feature_1` Nullable(String),\n `hse_feature_2` Nullable(String),\n `hse_feature_3` Nullable(String),\n `oth_feature_1` Nullable(String),\n `oth_feature_2` Nullable(String),\n `oth_feature_3` Nullable(String),\n `shp_feature_1` Nullable(String),\n `shp_feature_2` Nullable(String),\n `shp_feature_3` Nullable(String),\n `other_property_details` Nullable(String),\n `Properties_description` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types (\n `feature_type_code` Nullable(String),\n `feature_type_name` Nullable(String),\n `Ref_Feature_Types_description` Nullable(String),\n `Ref_Feature_Types_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Feature_Types_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types (\n `property_type_code` Nullable(String),\n `property_type_description` Nullable(String),\n `property_type_description_embedding` Array(Float32)\n);\nCREATE TABLE Ref_Property_Types_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Ref_Property_Types_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the top 5 feature IDs that relate to amenity features enhancing comfort, like having pools? I need them for an upcoming report!\n\nLet's think step by step!\n" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance\nFROM player_award p\nJOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the player IDs of the 10 players who excelled the most during the baseball season?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top performance during the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best players of the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional achievements in baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Most successful players in the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players with outstanding contributions in the baseball season') AS ref_vec_0\n\nSELECT p.player_id, distance(p.notes_embedding, ref_vec_0) AS distance FROM player_award p JOIN player pl ON toString(p.player_id) = toString(pl.player_id)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the player IDs of the 10 players who excelled the most during the baseball season?\n\nLet's think step by step!\n" + }, + { + "db_id": "baseball_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned university located in a major city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance\nFROM college\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which college is the most renowned and is situated in a major city?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A prestigious college located in a major urban area') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A famous college situated in a large city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An acclaimed university in a prominent city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known college in a big metropolitan area') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A distinguished university located in a major city') AS ref_vec_0\n\nSELECT college_id, distance(college.college_description_embedding, ref_vec_0) AS distance FROM college\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE all_star (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `game_num` Nullable(Int64),\n `game_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `gp` Nullable(Decimal(38, 6)),\n `starting_pos` Nullable(Decimal(38, 6))\n);\nCREATE TABLE appearances (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `g_all` Nullable(Decimal(38, 6)),\n `gs` Nullable(Decimal(38, 6)),\n `g_batting` Nullable(Int64),\n `g_defense` Nullable(Decimal(38, 6)),\n `g_p` Nullable(Int64),\n `g_c` Nullable(Int64),\n `g_1b` Nullable(Int64),\n `g_2b` Nullable(Int64),\n `g_3b` Nullable(Int64),\n `g_ss` Nullable(Int64),\n `g_lf` Nullable(Int64),\n `g_cf` Nullable(Int64),\n `g_rf` Nullable(Int64),\n `g_of` Nullable(Int64),\n `g_dh` Nullable(Decimal(38, 6)),\n `g_ph` Nullable(Decimal(38, 6)),\n `g_pr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Decimal(38, 6)),\n `r` Nullable(Decimal(38, 6)),\n `h` Nullable(Decimal(38, 6)),\n `double` Nullable(Decimal(38, 6)),\n `triple` Nullable(Decimal(38, 6)),\n `hr` Nullable(Decimal(38, 6)),\n `rbi` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Decimal(38, 6)),\n `so` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE batting_postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `player_id` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `g` Nullable(Int64),\n `ab` Nullable(Int64),\n `r` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `rbi` Nullable(Int64),\n `sb` Nullable(Int64),\n `cs` Nullable(Decimal(38, 6)),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `ibb` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE college (\n `college_id` Nullable(String),\n `name_full` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE fielding (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Decimal(38, 6)),\n `a` Nullable(Decimal(38, 6)),\n `e` Nullable(Decimal(38, 6)),\n `dp` Nullable(Decimal(38, 6)),\n `pb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `zr` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_outfield (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `glf` Nullable(Decimal(38, 6)),\n `gcf` Nullable(Decimal(38, 6)),\n `grf` Nullable(Decimal(38, 6))\n);\nCREATE TABLE fielding_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `round` Nullable(String),\n `pos` Nullable(String),\n `g` Nullable(Int64),\n `gs` Nullable(Decimal(38, 6)),\n `inn_outs` Nullable(Decimal(38, 6)),\n `po` Nullable(Int64),\n `a` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Int64),\n `tp` Nullable(Int64),\n `pb` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6))\n);\nCREATE TABLE hall_of_fame (\n `player_id` Nullable(String),\n `yearid` Nullable(Int64),\n `votedby` Nullable(String),\n `ballots` Nullable(Float64),\n `needed` Nullable(Float64),\n `votes` Nullable(Float64),\n `inducted` Nullable(String),\n `category` Nullable(String),\n `needed_note` Nullable(String),\n `hall_of_fame_description` Nullable(String),\n `hall_of_fame_description_embedding` Array(Float32)\n);\nCREATE TABLE home_game (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `park_id` Nullable(String),\n `span_first` Nullable(String),\n `span_last` Nullable(String),\n `games` Nullable(Int64),\n `openings` Nullable(Int64),\n `attendance` Nullable(Int64)\n);\nCREATE TABLE manager (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Float64),\n `plyr_mgr` Nullable(String),\n `manager_description` Nullable(String),\n `manager_description_embedding` Array(Float32)\n);\nCREATE TABLE manager_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(Float64),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE manager_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Int64),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Int64)\n);\nCREATE TABLE manager_half (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `inseason` Nullable(Int64),\n `half` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `rank` Nullable(Int64)\n);\nCREATE TABLE park (\n `park_id` Nullable(String),\n `park_name` Nullable(String),\n `park_alias` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `park_description` Nullable(String),\n `park_description_embedding` Array(Float32)\n);\nCREATE TABLE pitching (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `stint` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Decimal(38, 6)),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(Decimal(38, 6)),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Int64),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Decimal(38, 6)),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE pitching_postseason (\n `player_id` Nullable(String),\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `g` Nullable(Int64),\n `gs` Nullable(Int64),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `h` Nullable(Int64),\n `er` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Int64),\n `baopp` Nullable(String),\n `era` Nullable(Decimal(38, 6)),\n `ibb` Nullable(Decimal(38, 6)),\n `wp` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `bk` Nullable(Decimal(38, 6)),\n `bfp` Nullable(Decimal(38, 6)),\n `gf` Nullable(Int64),\n `r` Nullable(Int64),\n `sh` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `g_idp` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player (\n `player_id` Nullable(String),\n `birth_year` Nullable(Decimal(38, 6)),\n `birth_month` Nullable(Decimal(38, 6)),\n `birth_day` Nullable(Decimal(38, 6)),\n `birth_country` Nullable(String),\n `birth_state` Nullable(String),\n `birth_city` Nullable(String),\n `death_year` Nullable(Decimal(38, 6)),\n `death_month` Nullable(Decimal(38, 6)),\n `death_day` Nullable(Decimal(38, 6)),\n `death_country` Nullable(String),\n `death_state` Nullable(String),\n `death_city` Nullable(String),\n `name_first` Nullable(String),\n `name_last` Nullable(String),\n `name_given` Nullable(String),\n `weight` Nullable(Decimal(38, 6)),\n `height` Nullable(Decimal(38, 6)),\n `bats` Nullable(String),\n `throws` Nullable(String),\n `debut` Nullable(String),\n `final_game` Nullable(String),\n `retro_id` Nullable(String),\n `bbref_id` Nullable(String),\n `player_description` Nullable(String)\n);\nCREATE TABLE player_award (\n `player_id` Nullable(String),\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `tie` Nullable(String),\n `notes` Nullable(String),\n `notes_embedding` Array(Float32)\n);\nCREATE TABLE player_award_vote (\n `award_id` Nullable(String),\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `points_won` Nullable(Decimal(38, 6)),\n `points_max` Nullable(Int64),\n `votes_first` Nullable(Decimal(38, 6))\n);\nCREATE TABLE player_college (\n `player_id` Nullable(String),\n `college_id` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE postseason (\n `year` Nullable(Int64),\n `round` Nullable(String),\n `team_id_winner` Nullable(String),\n `league_id_winner` Nullable(String),\n `team_id_loser` Nullable(String),\n `league_id_loser` Nullable(String),\n `wins` Nullable(Int64),\n `losses` Nullable(Int64),\n `ties` Nullable(Int64)\n);\nCREATE TABLE salary (\n `year` Nullable(Int64),\n `team_id` Nullable(String),\n `league_id` Nullable(String),\n `player_id` Nullable(String),\n `salary` Nullable(Int64)\n);\nCREATE TABLE team (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `franchise_id` Nullable(String),\n `div_id` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `ghome` Nullable(Decimal(38, 6)),\n `w` Nullable(Int64),\n `l` Nullable(Int64),\n `div_win` Nullable(String),\n `wc_win` Nullable(String),\n `lg_win` Nullable(String),\n `ws_win` Nullable(String),\n `r` Nullable(Int64),\n `ab` Nullable(Int64),\n `h` Nullable(Int64),\n `double` Nullable(Int64),\n `triple` Nullable(Int64),\n `hr` Nullable(Int64),\n `bb` Nullable(Int64),\n `so` Nullable(Decimal(38, 6)),\n `sb` Nullable(Decimal(38, 6)),\n `cs` Nullable(Decimal(38, 6)),\n `hbp` Nullable(Decimal(38, 6)),\n `sf` Nullable(Decimal(38, 6)),\n `ra` Nullable(Int64),\n `er` Nullable(Int64),\n `era` Nullable(Decimal(38, 6)),\n `cg` Nullable(Int64),\n `sho` Nullable(Int64),\n `sv` Nullable(Int64),\n `ipouts` Nullable(Int64),\n `ha` Nullable(Int64),\n `hra` Nullable(Int64),\n `bba` Nullable(Int64),\n `soa` Nullable(Int64),\n `e` Nullable(Int64),\n `dp` Nullable(Decimal(38, 6)),\n `fp` Nullable(Decimal(38, 6)),\n `name` Nullable(String),\n `park` Nullable(String),\n `attendance` Nullable(Decimal(38, 6)),\n `bpf` Nullable(Int64),\n `ppf` Nullable(Int64),\n `team_id_br` Nullable(String),\n `team_id_lahman45` Nullable(String),\n `team_id_retro` Nullable(String),\n `team_description` Nullable(String)\n);\nCREATE TABLE team_franchise (\n `franchise_id` Nullable(String),\n `franchise_name` Nullable(String),\n `active` Nullable(String),\n `na_assoc` Nullable(String),\n `team_franchise_description` Nullable(String),\n `team_franchise_description_embedding` Array(Float32)\n);\nCREATE TABLE team_half (\n `year` Nullable(Int64),\n `league_id` Nullable(String),\n `team_id` Nullable(String),\n `half` Nullable(Int64),\n `div_id` Nullable(String),\n `div_win` Nullable(String),\n `rank` Nullable(Int64),\n `g` Nullable(Int64),\n `w` Nullable(Int64),\n `l` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me which college is the most renowned and is situated in a major city?\n\nLet's think step by step!\n" + }, + { + "db_id": "manufacturer", + "sql": "WITH ManufacturerDetails AS (\n SELECT \n m.Manufacturer_ID AS Manufacturer_ID, \n m.Name AS Manufacturer_Name, \n m.Num_of_Factories AS Num_of_Factories, \n m.Num_of_Shops AS Num_of_Shops\n FROM manufacturer m\n), FurnitureDetails AS (\n SELECT \n f.Furniture_ID AS Furniture_ID,\n f.Name AS Furniture_Name,\n f.Num_of_Component AS Num_of_Component,\n f.Market_Rate AS Market_Rate\n FROM furniture f\n), LatestManufacturerFurniture AS (\n SELECT \n fm.Manufacturer_ID AS Manufacturer_ID, \n fm.Furniture_ID AS Furniture_ID,\n fm.Price_in_Dollar AS Price_in_Dollar\n FROM furniture_manufacte fm\n INNER JOIN ManufacturerDetails md ON toString(fm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n WHERE fm.Price_in_Dollar > 1000 \n), CombinedData AS (\n SELECT \n lm.Manufacturer_ID AS Manufacturer_ID,\n lm.Furniture_ID AS Furniture_ID,\n md.Manufacturer_Name AS Manufacturer_Name,\n fd.Furniture_Name AS Furniture_Name,\n lm.Price_in_Dollar AS Price_in_Dollar,\n fd.Market_Rate AS Market_Rate,\n ROW_NUMBER() OVER (PARTITION BY lm.Manufacturer_ID ORDER BY lm.Price_in_Dollar DESC) AS rn\n FROM LatestManufacturerFurniture lm\n INNER JOIN ManufacturerDetails md ON toString(lm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n INNER JOIN FurnitureDetails fd ON toString(lm.Furniture_ID) = toString(fd.Furniture_ID)\n)\n\n\nSELECT Manufacturer_Name\nFROM CombinedData\nWHERE rn = 1\nORDER BY Manufacturer_Name;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which companies are crafting the priciest furniture pieces that stand out from the rest?", + "external_knowledge": "In this context, vector operations like \"MATCH\" or ANN search are not utilized. The query involves typical SQL operations like filtering, joining, and ordering dataset entries based on specific criteria, such as price. When dealing with vector search operations, concepts like Euclidean distance and k-nearest neighbors are crucial, but they are not applicable here as the query does not perform such tasks. The focus is on identifying items with high monetary value rather than similarity or distance metrics.", + "sql_candidate": [ + "WITH ManufacturerDetails AS (\n SELECT \n m.Manufacturer_ID AS Manufacturer_ID, \n m.Name AS Manufacturer_Name, \n m.Num_of_Factories AS Num_of_Factories, \n m.Num_of_Shops AS Num_of_Shops\n FROM manufacturer m\n), FurnitureDetails AS (\n SELECT \n f.Furniture_ID AS Furniture_ID,\n f.Name AS Furniture_Name,\n f.Num_of_Component AS Num_of_Component,\n f.Market_Rate AS Market_Rate\n FROM furniture f\n), LatestManufacturerFurniture AS (\n SELECT \n fm.Manufacturer_ID AS Manufacturer_ID, \n fm.Furniture_ID AS Furniture_ID,\n fm.Price_in_Dollar AS Price_in_Dollar\n FROM furniture_manufacte fm\n INNER JOIN ManufacturerDetails md ON toString(fm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n WHERE fm.Price_in_Dollar > 1000 \n), CombinedData AS (\n SELECT \n lm.Manufacturer_ID AS Manufacturer_ID,\n lm.Furniture_ID AS Furniture_ID,\n md.Manufacturer_Name AS Manufacturer_Name,\n fd.Furniture_Name AS Furniture_Name,\n lm.Price_in_Dollar AS Price_in_Dollar,\n fd.Market_Rate AS Market_Rate,\n ROW_NUMBER() OVER (PARTITION BY lm.Manufacturer_ID ORDER BY lm.Price_in_Dollar DESC) AS rn\n FROM LatestManufacturerFurniture lm\n INNER JOIN ManufacturerDetails md ON toString(lm.Manufacturer_ID) = toString(md.Manufacturer_ID)\n INNER JOIN FurnitureDetails fd ON toString(lm.Furniture_ID) = toString(fd.Furniture_ID)\n)\n\n\nSELECT Manufacturer_Name\nFROM CombinedData\nWHERE rn = 1\nORDER BY Manufacturer_Name;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE furniture (\n `Furniture_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Num_of_Component` Nullable(Int64),\n `Market_Rate` Nullable(Float64),\n `furniture_description` Nullable(String)\n);\nCREATE TABLE furniture_manufacte (\n `Manufacturer_ID` Nullable(Int64),\n `Furniture_ID` Nullable(Int64),\n `Price_in_Dollar` Nullable(Float64)\n);\nCREATE TABLE manufacturer (\n `Manufacturer_ID` Nullable(Int64),\n `Open_Year` Nullable(Float64),\n `Name` Nullable(String),\n `Num_of_Factories` Nullable(Int64),\n `Num_of_Shops` Nullable(Int64),\n `manufacturer_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE furniture (\n `Furniture_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Num_of_Component` Nullable(Int64),\n `Market_Rate` Nullable(Float64),\n `furniture_description` Nullable(String)\n);\nCREATE TABLE furniture_manufacte (\n `Manufacturer_ID` Nullable(Int64),\n `Furniture_ID` Nullable(Int64),\n `Price_in_Dollar` Nullable(Float64)\n);\nCREATE TABLE manufacturer (\n `Manufacturer_ID` Nullable(Int64),\n `Open_Year` Nullable(Float64),\n `Name` Nullable(String),\n `Num_of_Factories` Nullable(Int64),\n `Num_of_Shops` Nullable(Int64),\n `manufacturer_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn this context, vector operations like \"MATCH\" or ANN search are not utilized. The query involves typical SQL operations like filtering, joining, and ordering dataset entries based on specific criteria, such as price. When dealing with vector search operations, concepts like Euclidean distance and k-nearest neighbors are crucial, but they are not applicable here as the query does not perform such tasks. The focus is on identifying items with high monetary value rather than similarity or distance metrics.\nWhich companies are crafting the priciest furniture pieces that stand out from the rest?\n\nLet's think step by step!\n" + }, + { + "db_id": "concert_singer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A large stadium located in the city center with frequent concerts') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance\nFROM stadium\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the bustling orchestra of the city center, where might one find a grand stage that frequently echoes with the melodies of concerts?", + "external_knowledge": "- The `MATCH` operator performs an approximate nearest neighbor (ANN) search, which finds the closest match to a given vector by comparing distances. \n- Vectors are typically compared using Euclidean distance (L2 norm), where similarity increases as distance decreases. \n- The `lembed('all-MiniLM-L6-v2', ...)` function transforms the provided textual description into a vector representation, facilitating this search for matching entities in the database. \n- The description being matched indicates a stadium characterized by size, central location, and concert frequency, which are key elements in the search.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent venue in the city center known for hosting concerts frequently') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A central city stadium with regular concert events') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A major performance venue in the heart of the city with frequent musical events') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A large performance space in downtown often featuring concerts') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A central location for concerts in the city center') AS ref_vec_0\n\nSELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator performs an approximate nearest neighbor (ANN) search, which finds the closest match to a given vector by comparing distances. \n- Vectors are typically compared using Euclidean distance (L2 norm), where similarity increases as distance decreases. \n- The `lembed('all-MiniLM-L6-v2', ...)` function transforms the provided textual description into a vector representation, facilitating this search for matching entities in the database. \n- The description being matched indicates a stadium characterized by size, central location, and concert frequency, which are key elements in the search.\nIn the bustling orchestra of the city center, where might one find a grand stage that frequently echoes with the melodies of concerts?\n\nLet's think step by step!\n" + }, + { + "db_id": "concert_singer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A large stadium with high attendance and modern facilities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance \nFROM stadium\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the stadium that perfectly fits the vibe of being large, having high attendance, and featuring modern facilities? I'd love to know its ID and name!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A spacious stadium known for its large crowds and state-of-the-art amenities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A modern, large-capacity stadium with high visitor numbers') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A big stadium with excellent attendance and contemporary facilities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A large venue with high footfall and modern infrastructure') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A stadium featuring a large size, significant attendance, and up-to-date facilities') AS ref_vec_0\n\nSELECT Stadium_ID, Name, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you help me find the stadium that perfectly fits the vibe of being large, having high attendance, and featuring modern facilities? I'd love to know its ID and name!\n\nLet's think step by step!\n" + }, + { + "db_id": "election_representative", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An influential representative known for reform policies.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT \n r.Representative_ID AS Representative_ID, \n r.Name AS Name, \n r.State AS State, \n distance(r.representative_description_embedding, ref_vec_0) AS distance\n FROM \n representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n rm.Representative_ID AS Representative_ID, \n rm.Name AS Name, \n e.Votes AS Votes\nFROM \n RepresentativeMatch rm\nJOIN \n election e ON toString(rm.Representative_ID) = toString(e.Representative_ID)\nORDER BY \n e.Votes DESC\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 4, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Find the top 5 influential representatives known for reform policies and list their vote counts.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top representatives advocating for reform initiatives.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading figures in reform policy advocacy.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Influential lawmakers focused on reform agendas.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prominent representatives pushing for policy reforms.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Key advocates of reform policies in the legislature.') AS ref_vec_0,\n\nRepresentativeMatch AS (\n SELECT r.Representative_ID, r.Name, r.State, distance(r.representative_description_embedding, ref_vec_0) AS distance FROM representative r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rm.Representative_ID, rm.Name, e.Votes FROM RepresentativeMatch rm JOIN election e ON toString(rm.Representative_ID) = toString(e.Representative_ID) ORDER BY e.Votes DESC LIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Representative_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Votes` Nullable(Float64),\n `Vote_Percent` Nullable(Float64),\n `Seats` Nullable(Float64),\n `Place` Nullable(Float64)\n);\nCREATE TABLE representative (\n `Representative_ID` Nullable(Int64),\n `Name` Nullable(String),\n `State` Nullable(String),\n `Party` Nullable(String),\n `Lifespan` Nullable(String),\n `representative_description` Nullable(String),\n `representative_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Representative_ID` Nullable(Int64),\n `Date` Nullable(String),\n `Votes` Nullable(Float64),\n `Vote_Percent` Nullable(Float64),\n `Seats` Nullable(Float64),\n `Place` Nullable(Float64)\n);\nCREATE TABLE representative (\n `Representative_ID` Nullable(Int64),\n `Name` Nullable(String),\n `State` Nullable(String),\n `Party` Nullable(String),\n `Lifespan` Nullable(String),\n `representative_description` Nullable(String),\n `representative_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nFind the top 5 influential representatives known for reform policies and list their vote counts.\n\nLet's think step by step!\n" + }, + { + "db_id": "concert_singer", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Large stadium in New York with a seating capacity over 50,000') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance\n FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID\n FROM singer\n WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name\nFROM concert c\nJOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID)\nJOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID)\nJOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID)\nWHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you tell me the names of concerts that feature American singers and take place in a large stadium located in New York with a seating capacity of over 50,000?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Major venue in NYC with over 50,000 seats') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Large-capacity stadium in New York with American performers') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York stadium with high seating capacity for concerts') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'New York concert venues with over 50,000 seats') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Stadium in New York with large seating for American concerts') AS ref_vec_0,\n\nRelevantStadiums AS (\n SELECT Stadium_ID, distance(stadium.stadium_description_embedding, ref_vec_0) AS distance FROM stadium\n ORDER BY distance\n LIMIT 1\n),\n\nRelevantSingers AS (\n SELECT Singer_ID FROM singer WHERE Country = 'USA'\n)\n\nSELECT c.concert_Name FROM concert c JOIN stadium s ON toString(c.Stadium_ID) = toString(s.Stadium_ID) JOIN singer_in_concert sic ON toString(c.concert_ID) = toString(sic.concert_ID) JOIN RelevantSingers rs ON toString(sic.Singer_ID) = toString(rs.Singer_ID) WHERE s.Stadium_ID IN (SELECT Stadium_ID FROM RelevantStadiums);" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE concert (\n `concert_ID` Nullable(Int64),\n `concert_Name` Nullable(String),\n `Theme` Nullable(String),\n `Stadium_ID` Nullable(String),\n `Year` Nullable(String),\n `concert_description` Nullable(String),\n `Theme_embedding` Array(Float32),\n `concert_description_embedding` Array(Float32)\n);\nCREATE TABLE singer (\n `Singer_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `Song_Name` Nullable(String),\n `Song_release_year` Nullable(String),\n `Age` Nullable(Int64),\n `Is_male` Nullable(String),\n `singer_description` Nullable(String),\n `singer_description_embedding` Array(Float32)\n);\nCREATE TABLE singer_in_concert (\n `concert_ID` Nullable(Int64),\n `Singer_ID` Nullable(String)\n);\nCREATE TABLE stadium (\n `Stadium_ID` Nullable(Int64),\n `Location` Nullable(String),\n `Name` Nullable(String),\n `Capacity` Nullable(Int64),\n `Highest` Nullable(Int64),\n `Lowest` Nullable(Int64),\n `Average` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the names of concerts that feature American singers and take place in a large stadium located in New York with a seating capacity of over 50,000?\n\nLet's think step by step!\n" + }, + { + "db_id": "swimming", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exciting swimming event held in a large stadium') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Outstanding performance in a competitive 200 meters race') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id\nFROM e_filtered AS e\nJOIN record r ON toString(e.ID) = toString(r.Event_ID)\nJOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "sql_result_column_count": 1, + "sql_result_rows_count": 9, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find a selection of events that took place in a large stadium and featured swimmers known for their exceptional performances in 200-meter races?", + "external_knowledge": "- The `MATCH` operator is used here to perform an approximate nearest neighbor (ANN) search on vector embeddings.\n- Embeddings are high-dimensional representations of text used to capture semantic meaning.\n- The `lembed()` function involves transforming textual descriptions into these embeddings using a model like 'all-MiniLM-L6-v2'.\n- The query uses `k=10` to limit the search to the top 10 events and `k=5` for the top 5 swimmers that match the given descriptions.\n- In vector space, similarity is determined using Euclidean distance; lower distances indicate higher similarity.\n- Descriptions like \"an exciting swimming event held in a large stadium\" and \"outstanding performance in a competitive 200 meters race\" are mapped to vectors encapsulating these concepts for comparison.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A major swimming competition hosted in a grand stadium') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Notable achievements in 200-meter swimming events') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A large-scale stadium event featuring top swimmers') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Exceptional 200m race performances by swimmers') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent swimming meet in a vast arena') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', '200-meter race specialists with remarkable records') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'A significant swimming event occurring in a large venue') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Elite swimmers known for 200m excellence') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);", + "WITH\n lembed('all-MiniLM-L6-v2', 'An impressive swimming event in a massive stadium') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Swimmers with outstanding 200-meter race skills') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(event_description_embedding, ref_vec_0) AS distance\n FROM event\n\n ORDER BY distance\n LIMIT 10\n),\n\ns_filtered AS (\n SELECT\n *,\n distance(swimmer_description_embedding, ref_vec_1) AS distance\n FROM swimmer\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS event_id FROM e_filtered AS e JOIN record r ON toString(e.ID) = toString(r.Event_ID) JOIN s_filtered AS s ON toString(r.Swimmer_ID) = toString(s.ID);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE event (\n `ID` Nullable(Int64),\n `Name` Nullable(String),\n `Stadium_ID` Nullable(Int64),\n `Year` Nullable(String),\n `event_description` Nullable(String),\n `event_description_embedding` Array(Float32)\n);\nCREATE TABLE event_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE record (\n `ID` Nullable(Int64),\n `Result` Nullable(String),\n `Swimmer_ID` Nullable(Int64),\n `Event_ID` Nullable(Int64)\n);\nCREATE TABLE stadium (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `Capacity` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `Opening_year` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);\nCREATE TABLE stadium_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE swimmer (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `Nationality` Nullable(String),\n `meter_100` Nullable(Float64),\n `meter_200` Nullable(String),\n `meter_300` Nullable(String),\n `meter_400` Nullable(String),\n `meter_500` Nullable(String),\n `meter_600` Nullable(String),\n `meter_700` Nullable(String),\n `Time` Nullable(String),\n `swimmer_description` Nullable(String),\n `swimmer_description_embedding` Array(Float32)\n);\nCREATE TABLE swimmer_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE event (\n `ID` Nullable(Int64),\n `Name` Nullable(String),\n `Stadium_ID` Nullable(Int64),\n `Year` Nullable(String),\n `event_description` Nullable(String),\n `event_description_embedding` Array(Float32)\n);\nCREATE TABLE event_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE event_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE record (\n `ID` Nullable(Int64),\n `Result` Nullable(String),\n `Swimmer_ID` Nullable(Int64),\n `Event_ID` Nullable(Int64)\n);\nCREATE TABLE stadium (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `Capacity` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `Opening_year` Nullable(Int64),\n `stadium_description` Nullable(String),\n `stadium_description_embedding` Array(Float32)\n);\nCREATE TABLE stadium_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE stadium_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE swimmer (\n `ID` Nullable(Int64),\n `name` Nullable(String),\n `Nationality` Nullable(String),\n `meter_100` Nullable(Float64),\n `meter_200` Nullable(String),\n `meter_300` Nullable(String),\n `meter_400` Nullable(String),\n `meter_500` Nullable(String),\n `meter_600` Nullable(String),\n `meter_700` Nullable(String),\n `Time` Nullable(String),\n `swimmer_description` Nullable(String),\n `swimmer_description_embedding` Array(Float32)\n);\nCREATE TABLE swimmer_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatachunks11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext10 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_metadatatext11 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE swimmer_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator is used here to perform an approximate nearest neighbor (ANN) search on vector embeddings.\n- Embeddings are high-dimensional representations of text used to capture semantic meaning.\n- The `lembed()` function involves transforming textual descriptions into these embeddings using a model like 'all-MiniLM-L6-v2'.\n- The query uses `k=10` to limit the search to the top 10 events and `k=5` for the top 5 swimmers that match the given descriptions.\n- In vector space, similarity is determined using Euclidean distance; lower distances indicate higher similarity.\n- Descriptions like \"an exciting swimming event held in a large stadium\" and \"outstanding performance in a competitive 200 meters race\" are mapped to vectors encapsulating these concepts for comparison.\nCan you find a selection of events that took place in a large stadium and featured swimmers known for their exceptional performances in 200-meter races?\n\nLet's think step by step!\n" + }, + { + "db_id": "roller_coaster", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A roller coaster with a thrilling experience and high speed') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance\nFROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Top 5 roller coasters known for thrilling experiences and high speeds, list their IDs and names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exciting roller coasters with high velocity and adrenaline-pumping rides') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Roller coasters offering exhilarating speeds and thrilling experiences') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-speed roller coasters known for their thrilling rides') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Roller coasters renowned for fast and thrilling rides') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top roller coasters with thrilling high-speed experiences') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, Name, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Area` Nullable(Int64),\n `Languages` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE country_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE roller_coaster (\n `Roller_Coaster_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Park` Nullable(String),\n `Country_ID` Nullable(Int64),\n `Length` Nullable(Float64),\n `Height` Nullable(Float64),\n `Speed` Nullable(String),\n `Opened` Nullable(String),\n `Status` Nullable(String),\n `roller_coaster_description` Nullable(String),\n `roller_coaster_description_embedding` Array(Float32)\n);\nCREATE TABLE roller_coaster_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE country (\n `Country_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Area` Nullable(Int64),\n `Languages` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE country_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE roller_coaster (\n `Roller_Coaster_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Park` Nullable(String),\n `Country_ID` Nullable(Int64),\n `Length` Nullable(Float64),\n `Height` Nullable(Float64),\n `Speed` Nullable(String),\n `Opened` Nullable(String),\n `Status` Nullable(String),\n `roller_coaster_description` Nullable(String),\n `roller_coaster_description_embedding` Array(Float32)\n);\nCREATE TABLE roller_coaster_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nTop 5 roller coasters known for thrilling experiences and high speeds, list their IDs and names.\n\nLet's think step by step!\n" + }, + { + "db_id": "roller_coaster", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Wooden roller coaster with a thrilling experience and unique design') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance \nFROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the roller coaster that best embodies a thrilling experience and unique design as a wooden coaster, and provide its ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Exciting wooden coaster with innovative design') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Wooden coaster offering a thrilling and unique ride') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Unique wooden coaster that provides an exhilarating experience') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Thrilling wooden roller coaster with distinctive design') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Wooden roller coaster known for its thrilling and unique features') AS ref_vec_0\n\nSELECT Roller_Coaster_ID, distance(roller_coaster.roller_coaster_description_embedding, ref_vec_0) AS distance FROM roller_coaster\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Area` Nullable(Int64),\n `Languages` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE country_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE roller_coaster (\n `Roller_Coaster_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Park` Nullable(String),\n `Country_ID` Nullable(Int64),\n `Length` Nullable(Float64),\n `Height` Nullable(Float64),\n `Speed` Nullable(String),\n `Opened` Nullable(String),\n `Status` Nullable(String),\n `roller_coaster_description` Nullable(String),\n `roller_coaster_description_embedding` Array(Float32)\n);\nCREATE TABLE roller_coaster_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE country (\n `Country_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Population` Nullable(Int64),\n `Area` Nullable(Int64),\n `Languages` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE country_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE country_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE roller_coaster (\n `Roller_Coaster_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Park` Nullable(String),\n `Country_ID` Nullable(Int64),\n `Length` Nullable(Float64),\n `Height` Nullable(Float64),\n `Speed` Nullable(String),\n `Opened` Nullable(String),\n `Status` Nullable(String),\n `roller_coaster_description` Nullable(String),\n `roller_coaster_description_embedding` Array(Float32)\n);\nCREATE TABLE roller_coaster_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatachunks09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext08 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_metadatatext09 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE roller_coaster_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the roller coaster that best embodies a thrilling experience and unique design as a wooden coaster, and provide its ID.\n\nLet's think step by step!\n" + }, + { + "db_id": "college_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A student in the Computer Science department.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance\nFROM student\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the name of the student who best matches the description of being in the Computer Science department?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A Computer Science department student.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Student belonging to Computer Science.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Enrolled in Computer Science department.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Computer Science student.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A learner in the Computer Science field.') AS ref_vec_0\n\nSELECT name, distance(student.student_description_embedding, ref_vec_0) AS distance FROM student\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the name of the student who best matches the description of being in the Computer Science department?\n\nLet's think step by step!\n" + }, + { + "db_id": "college_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An introductory course in programming with a focus on C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance\nFROM course\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "I need to find the course title of the introductory programming course that focuses on the C language. Can you provide the best match for this in terms of course description?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Introductory programming course centered around C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Beginner programming class with emphasis on C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Programming fundamentals course focusing on C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Intro to programming using C language.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Basic programming course with C language focus.') AS ref_vec_0\n\nSELECT title, distance(course.course_description_embedding, ref_vec_0) AS distance FROM course\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE advisor (\n `s_ID` Nullable(String),\n `i_ID` Nullable(String)\n);\nCREATE TABLE classroom (\n `building` Nullable(String),\n `room_number` Nullable(String),\n `capacity` Nullable(Float64),\n `classroom_description` Nullable(String),\n `classroom_description_embedding` Array(Float32)\n);\nCREATE TABLE classroom_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE classroom_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE course (\n `course_id` Nullable(String),\n `title` Nullable(String),\n `dept_name` Nullable(String),\n `credits` Nullable(Float64),\n `course_description` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE course_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE course_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE department (\n `dept_name` Nullable(String),\n `building` Nullable(String),\n `budget` Nullable(Float64),\n `department_description` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE department_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE department_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE instructor (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `salary` Nullable(Float64),\n `instructor_description` Nullable(String),\n `instructor_description_embedding` Array(Float32)\n);\nCREATE TABLE instructor_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE instructor_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE prereq (\n `course_id` Nullable(String),\n `prereq_id` Nullable(String)\n);\nCREATE TABLE section (\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Float64),\n `building` Nullable(String),\n `room_number` Nullable(String),\n `time_slot_id` Nullable(String),\n `section_description` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE section_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE section_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE student (\n `ID` Nullable(String),\n `name` Nullable(String),\n `dept_name` Nullable(String),\n `tot_cred` Nullable(Float64),\n `student_description` Nullable(String),\n `student_description_embedding` Array(Float32)\n);\nCREATE TABLE student_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE student_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE takes (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6)),\n `grade` Nullable(String)\n);\nCREATE TABLE teaches (\n `ID` Nullable(String),\n `course_id` Nullable(String),\n `sec_id` Nullable(String),\n `semester` Nullable(String),\n `year` Nullable(Decimal(38, 6))\n);\nCREATE TABLE time_slot (\n `time_slot_id` Nullable(String),\n `day` Nullable(String),\n `start_hr` Nullable(Decimal(38, 6)),\n `start_min` Nullable(Decimal(38, 6)),\n `end_hr` Nullable(Decimal(38, 6)),\n `end_min` Nullable(Decimal(38, 6))\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI need to find the course title of the introductory programming course that focuses on the C language. Can you provide the best match for this in terms of course description?\n\nLet's think step by step!\n" + }, + { + "db_id": "book_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Black Sheep in a modern setting') AS ref_vec_0\n\nSELECT \n Book_ID, \n Title, distance(book.Title_embedding, ref_vec_0) AS distance\nFROM \n book\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Can you uncover the book that embodies the essence of a trailblazer, akin to a modern-day 'Black Sheep'?", + "external_knowledge": "The `MATCH` operator is used in vector searches to find items that are similar to a given vector representation, based on the concept of approximate nearest neighbor (ANN) search. In this context, vector embeddings are numerical representations of text used to compare semantic similarity. The model 'all-MiniLM-L6-v2' is employed for generating these embeddings, which allows for capturing intricate patterns in language. The phrase \"The Black Sheep in a modern setting\" serves as a metaphor, suggesting a book that depicts nonconformity or uniqueness against contemporary norms.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A pioneering spirit in contemporary literature') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The essence of a modern trailblazer') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An innovative thinker in today’s world') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A contemporary rebel with visionary ideas') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A groundbreaking figure in modern storytelling') AS ref_vec_0\n\nSELECT Book_ID, Title, distance(book.Title_embedding, ref_vec_0) AS distance FROM book\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE book (\n `Book_ID` Nullable(Int64),\n `Title` Nullable(String),\n `Issues` Nullable(Float64),\n `Writer` Nullable(String),\n `book_description` Nullable(String),\n `Title_embedding` Array(Float32),\n `book_description_embedding` Array(Float32)\n);\nCREATE TABLE publication (\n `Publication_ID` Nullable(Int64),\n `Book_ID` Nullable(Int64),\n `Publisher` Nullable(String),\n `Publication_Date` Nullable(String),\n `Price` Nullable(Float64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE book (\n `Book_ID` Nullable(Int64),\n `Title` Nullable(String),\n `Issues` Nullable(Float64),\n `Writer` Nullable(String),\n `book_description` Nullable(String),\n `Title_embedding` Array(Float32),\n `book_description_embedding` Array(Float32)\n);\nCREATE TABLE publication (\n `Publication_ID` Nullable(Int64),\n `Book_ID` Nullable(Int64),\n `Publisher` Nullable(String),\n `Publication_Date` Nullable(String),\n `Price` Nullable(Float64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator is used in vector searches to find items that are similar to a given vector representation, based on the concept of approximate nearest neighbor (ANN) search. In this context, vector embeddings are numerical representations of text used to compare semantic similarity. The model 'all-MiniLM-L6-v2' is employed for generating these embeddings, which allows for capturing intricate patterns in language. The phrase \"The Black Sheep in a modern setting\" serves as a metaphor, suggesting a book that depicts nonconformity or uniqueness against contemporary norms.\nCan you uncover the book that embodies the essence of a trailblazer, akin to a modern-day 'Black Sheep'?\n\nLet's think step by step!\n" + }, + { + "db_id": "hospital_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, a renowned cardiologist known for his expertise in heart disease management and surgical interventions.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance\n FROM Physician p\n JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName\n FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName\nFROM PhysicianAffiliation pa\nJOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID)\nORDER BY pa.PrimaryAffiliation DESC;", + "sql_result_column_count": 2, + "sql_result_rows_count": 11, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 10 doctors who are like Dr. John Smith, the awesome heart expert, and tell me what departments they're in? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, an exceptional cardiologist specializing in heart health and surgical treatments.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, a leading expert in cardiology and heart surgery.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, famous for his heart disease expertise and surgical skills.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, renowned for his proficiency in managing heart conditions and performing surgeries.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Dr. John Smith, a top cardiologist with expertise in heart disease management and surgery.') AS ref_vec_0,\n\nPhysicianAffiliation AS (\n SELECT p.EmployeeID, p.Name, a.Department, a.PrimaryAffiliation, distance(p.Physician_description_embedding, ref_vec_0) AS distance FROM Physician p JOIN Affiliated_With a ON toString(p.EmployeeID) = toString(a.Physician)\n ORDER BY distance\n LIMIT 10\n),\n\nDepartmentInfo AS (\n SELECT d.DepartmentID, d.Name AS DepartmentName FROM Department d\n)\n\nSELECT pa.Name, di.DepartmentName FROM PhysicianAffiliation pa JOIN DepartmentInfo di ON toString(pa.Department) = toString(di.DepartmentID) ORDER BY pa.PrimaryAffiliation DESC;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Affiliated_With (\n `Physician` Int64,\n `Department` Int64,\n `PrimaryAffiliation` String\n);\nCREATE TABLE Appointment (\n `AppointmentID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `PrepNurse` Nullable(Int64),\n `Physician` Nullable(Int64),\n `Start` Nullable(String),\n `End` Nullable(String),\n `ExaminationRoom` Nullable(String),\n `Appointment_description` Nullable(String),\n `Appointment_description_embedding` Array(Float32)\n);\nCREATE TABLE Block (\n `BlockFloor` Int64,\n `BlockCode` Int64\n);\nCREATE TABLE Department (\n `DepartmentID` Nullable(Int64),\n `Name` Nullable(String),\n `Head` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Medication (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Brand` Nullable(String),\n `Description` Nullable(String),\n `Medication_description` Nullable(String),\n `Medication_description_embedding` Array(Float32)\n);\nCREATE TABLE Nurse (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `Registered` Nullable(String),\n `SSN` Nullable(Int64),\n `Nurse_description` Nullable(String),\n `Nurse_description_embedding` Array(Float32)\n);\nCREATE TABLE On_Call (\n `Nurse` Int64,\n `BlockFloor` Int64,\n `BlockCode` Int64,\n `OnCallStart` Date,\n `OnCallEnd` Date\n);\nCREATE TABLE Patient (\n `SSN` Nullable(Int64),\n `Name` Nullable(String),\n `Address` Nullable(String),\n `Phone` Nullable(String),\n `InsuranceID` Nullable(Int64),\n `PCP` Nullable(Int64),\n `Patient_description` Nullable(String),\n `Patient_description_embedding` Array(Float32)\n);\nCREATE TABLE Physician (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `SSN` Nullable(Int64),\n `Physician_description` Nullable(String),\n `Physician_description_embedding` Array(Float32)\n);\nCREATE TABLE Prescribes (\n `Physician` Int64,\n `Patient` Int64,\n `Medication` Int64,\n `Date` Date,\n `Appointment` Nullable(Int64),\n `Dose` String\n);\nCREATE TABLE Procedures (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Cost` Nullable(Float64),\n `Procedures_description` Nullable(String),\n `Procedures_description_embedding` Array(Float32)\n);\nCREATE TABLE Room (\n `RoomNumber` Nullable(Int64),\n `RoomType` Nullable(String),\n `BlockFloor` Nullable(Int64),\n `BlockCode` Nullable(Int64),\n `Unavailable` Nullable(String),\n `Room_description` Nullable(String),\n `Room_description_embedding` Array(Float32)\n);\nCREATE TABLE Stay (\n `StayID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `Room` Nullable(Int64),\n `StayStart` Nullable(String),\n `StayEnd` Nullable(String),\n `Stay_description` Nullable(String),\n `Stay_description_embedding` Array(Float32)\n);\nCREATE TABLE Trained_In (\n `Physician` Int64,\n `Treatment` Int64,\n `CertificationDate` Date,\n `CertificationExpires` Date\n);\nCREATE TABLE Undergoes (\n `Patient` Int64,\n `Procedures` Int64,\n `Stay` Int64,\n `DateUndergoes` Date,\n `Physician` Int64,\n `AssistingNurse` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Affiliated_With (\n `Physician` Int64,\n `Department` Int64,\n `PrimaryAffiliation` String\n);\nCREATE TABLE Appointment (\n `AppointmentID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `PrepNurse` Nullable(Int64),\n `Physician` Nullable(Int64),\n `Start` Nullable(String),\n `End` Nullable(String),\n `ExaminationRoom` Nullable(String),\n `Appointment_description` Nullable(String),\n `Appointment_description_embedding` Array(Float32)\n);\nCREATE TABLE Block (\n `BlockFloor` Int64,\n `BlockCode` Int64\n);\nCREATE TABLE Department (\n `DepartmentID` Nullable(Int64),\n `Name` Nullable(String),\n `Head` Nullable(Int64),\n `Department_description` Nullable(String),\n `Department_description_embedding` Array(Float32)\n);\nCREATE TABLE Medication (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Brand` Nullable(String),\n `Description` Nullable(String),\n `Medication_description` Nullable(String),\n `Medication_description_embedding` Array(Float32)\n);\nCREATE TABLE Nurse (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `Registered` Nullable(String),\n `SSN` Nullable(Int64),\n `Nurse_description` Nullable(String),\n `Nurse_description_embedding` Array(Float32)\n);\nCREATE TABLE On_Call (\n `Nurse` Int64,\n `BlockFloor` Int64,\n `BlockCode` Int64,\n `OnCallStart` Date,\n `OnCallEnd` Date\n);\nCREATE TABLE Patient (\n `SSN` Nullable(Int64),\n `Name` Nullable(String),\n `Address` Nullable(String),\n `Phone` Nullable(String),\n `InsuranceID` Nullable(Int64),\n `PCP` Nullable(Int64),\n `Patient_description` Nullable(String),\n `Patient_description_embedding` Array(Float32)\n);\nCREATE TABLE Physician (\n `EmployeeID` Nullable(Int64),\n `Name` Nullable(String),\n `Position` Nullable(String),\n `SSN` Nullable(Int64),\n `Physician_description` Nullable(String),\n `Physician_description_embedding` Array(Float32)\n);\nCREATE TABLE Prescribes (\n `Physician` Int64,\n `Patient` Int64,\n `Medication` Int64,\n `Date` Date,\n `Appointment` Nullable(Int64),\n `Dose` String\n);\nCREATE TABLE Procedures (\n `Code` Nullable(Int64),\n `Name` Nullable(String),\n `Cost` Nullable(Float64),\n `Procedures_description` Nullable(String),\n `Procedures_description_embedding` Array(Float32)\n);\nCREATE TABLE Room (\n `RoomNumber` Nullable(Int64),\n `RoomType` Nullable(String),\n `BlockFloor` Nullable(Int64),\n `BlockCode` Nullable(Int64),\n `Unavailable` Nullable(String),\n `Room_description` Nullable(String),\n `Room_description_embedding` Array(Float32)\n);\nCREATE TABLE Stay (\n `StayID` Nullable(Int64),\n `Patient` Nullable(Int64),\n `Room` Nullable(Int64),\n `StayStart` Nullable(String),\n `StayEnd` Nullable(String),\n `Stay_description` Nullable(String),\n `Stay_description_embedding` Array(Float32)\n);\nCREATE TABLE Trained_In (\n `Physician` Int64,\n `Treatment` Int64,\n `CertificationDate` Date,\n `CertificationExpires` Date\n);\nCREATE TABLE Undergoes (\n `Patient` Int64,\n `Procedures` Int64,\n `Stay` Int64,\n `DateUndergoes` Date,\n `Physician` Int64,\n `AssistingNurse` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you help me find the top 10 doctors who are like Dr. John Smith, the awesome heart expert, and tell me what departments they're in? Thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "icfp_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exploring techniques for simplifying monadic equational reasoning') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance\nFROM Papers\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you help me out by finding the paper ID for the most relevant paper that talks about simplifying monadic equational reasoning?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Simplification methods in monadic equational logic') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Approaches to streamline monadic equational reasoning') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Monadic equational reasoning simplification strategies') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Techniques for simplifying reasoning in monadic equations') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Efficient methods for monadic equational reasoning simplification') AS ref_vec_0\n\nSELECT paperID, distance(Papers.title_embedding, ref_vec_0) AS distance FROM Papers\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Authors (\n `authID` Nullable(Int64),\n `lname` Nullable(String),\n `fname` Nullable(String),\n `Authors_description` Nullable(String),\n `Authors_description_embedding` Array(Float32)\n);\nCREATE TABLE Authorship (\n `authID` Nullable(Int64),\n `instID` Nullable(Int64),\n `paperID` Nullable(Int64),\n `authOrder` Nullable(Int64)\n);\nCREATE TABLE Inst (\n `instID` Nullable(Int64),\n `name` Nullable(String),\n `country` Nullable(String),\n `Inst_description` Nullable(String),\n `Inst_description_embedding` Array(Float32)\n);\nCREATE TABLE Papers (\n `paperID` Nullable(Int64),\n `title` Nullable(String),\n `Papers_description` Nullable(String),\n `title_embedding` Array(Float32),\n `Papers_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Authors (\n `authID` Nullable(Int64),\n `lname` Nullable(String),\n `fname` Nullable(String),\n `Authors_description` Nullable(String),\n `Authors_description_embedding` Array(Float32)\n);\nCREATE TABLE Authorship (\n `authID` Nullable(Int64),\n `instID` Nullable(Int64),\n `paperID` Nullable(Int64),\n `authOrder` Nullable(Int64)\n);\nCREATE TABLE Inst (\n `instID` Nullable(Int64),\n `name` Nullable(String),\n `country` Nullable(String),\n `Inst_description` Nullable(String),\n `Inst_description_embedding` Array(Float32)\n);\nCREATE TABLE Papers (\n `paperID` Nullable(Int64),\n `title` Nullable(String),\n `Papers_description` Nullable(String),\n `title_embedding` Array(Float32),\n `Papers_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you help me out by finding the paper ID for the most relevant paper that talks about simplifying monadic equational reasoning?\n\nLet's think step by step!\n" + }, + { + "db_id": "network_2", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'John is a software engineer living in San Francisco who enjoys hiking and playing the guitar.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance\n FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name\nFROM SimilarPersons SP\nJOIN PersonFriend PF ON toString(SP.name) = toString(PF.name)\nWHERE PF.year > 2015\nORDER BY SP.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who are the people who share similarities with John, especially in terms of interests and profession, and have formed friendships that started after 2015?", + "external_knowledge": "In vector operations, the `MATCH` operator is used to perform approximate nearest neighbor (ANN) searches with embeddings to identify items that are most similar to a given query. The `LIMIT` clause specifies how many similar items to return—in this case, the top 5. The `lembed` function generates vector embeddings based on text descriptions, which are then compared using Euclidean distance (L2 norm) by default. Greater similarity corresponds to smaller distance values. \"John is a software engineer living in San Francisco who enjoys hiking and playing the guitar\" is a description used to find other people with similar traits or activities, interpreted through the vector embeddings.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'John is a tech professional in Silicon Valley who loves outdoor activities and music.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John works in software development and is passionate about hiking and guitar playing.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John is a software engineer who enjoys outdoor sports and music hobbies.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John is an IT professional interested in hiking and playing musical instruments.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'John is a tech worker who loves exploring nature and playing the guitar.') AS ref_vec_0,\n\nSimilarPersons AS (\n SELECT name, distance(Person.Person_description_embedding, ref_vec_0) AS distance FROM Person\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT SP.name FROM SimilarPersons SP JOIN PersonFriend PF ON toString(SP.name) = toString(PF.name) WHERE PF.year > 2015 ORDER BY SP.distance;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Person (\n `name` Nullable(String),\n `age` Nullable(Int64),\n `city` Nullable(String),\n `gender` Nullable(String),\n `job` Nullable(String),\n `Person_description` Nullable(String),\n `Person_description_embedding` Array(Float32)\n);\nCREATE TABLE PersonFriend (\n `name` Nullable(String),\n `friend` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE Person_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Person (\n `name` Nullable(String),\n `age` Nullable(Int64),\n `city` Nullable(String),\n `gender` Nullable(String),\n `job` Nullable(String),\n `Person_description` Nullable(String),\n `Person_description_embedding` Array(Float32)\n);\nCREATE TABLE PersonFriend (\n `name` Nullable(String),\n `friend` Nullable(String),\n `year` Nullable(Int64)\n);\nCREATE TABLE Person_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Person_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn vector operations, the `MATCH` operator is used to perform approximate nearest neighbor (ANN) searches with embeddings to identify items that are most similar to a given query. The `LIMIT` clause specifies how many similar items to return—in this case, the top 5. The `lembed` function generates vector embeddings based on text descriptions, which are then compared using Euclidean distance (L2 norm) by default. Greater similarity corresponds to smaller distance values. \"John is a software engineer living in San Francisco who enjoys hiking and playing the guitar\" is a description used to find other people with similar traits or activities, interpreted through the vector embeddings.\nWho are the people who share similarities with John, especially in terms of interests and profession, and have formed friendships that started after 2015?\n\nLet's think step by step!\n" + }, + { + "db_id": "decoration_competition", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned educational institution in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Esteemed professor specializing in quantum physics') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Futuristic technology symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme\nFROM c_filtered AS c\nJOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID)\nJOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID)\nORDER BY c.Name, m.Name, r.Decoration_Theme;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the names of the top 3 colleges renowned as educational institutions in California, the names of the top 3 esteemed professors specializing in quantum physics affiliated with these colleges, and the themes of the top 3 futuristic technology symposiums they may be involved in. Ensure the results are ordered by the college names, member names, and decoration themes.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top educational institutions in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Leading quantum physics professors') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Innovative tech symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Prestigious colleges in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Distinguished quantum physics experts') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Advanced technology symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'California''''s top academic institutions') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Quantum physics specialists') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Cutting-edge tech symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading colleges in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Renowned quantum physicists') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Future tech symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Esteemed educational institutions in California') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top quantum physics lecturers') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Tech innovation symposium') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(college_description_embedding, ref_vec_0) AS distance\n FROM college\n\n ORDER BY distance\n LIMIT 3\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(member_description_embedding, ref_vec_1) AS distance\n FROM member\n\n ORDER BY distance\n LIMIT 3\n),\n\nr_filtered AS (\n SELECT\n *,\n distance(Decoration_Theme_embedding, ref_vec_2) AS distance\n FROM round\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.Name AS CollegeName, m.Name AS MemberName, r.Decoration_Theme AS DecorationTheme FROM c_filtered AS c JOIN m_filtered AS m ON toString(c.College_ID) = toString(m.College_ID) JOIN r_filtered AS r ON toString(m.Member_ID) = toString(r.Member_ID) ORDER BY c.Name, m.Name, r.Decoration_Theme;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE college (\n `College_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Leader_Name` Nullable(String),\n `College_Location` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `College_ID` Nullable(Int64),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE round (\n `Round_ID` Nullable(Int64),\n `Member_ID` Nullable(Int64),\n `Decoration_Theme` Nullable(String),\n `Rank_in_Round` Nullable(Int64),\n `Decoration_Theme_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE college (\n `College_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Leader_Name` Nullable(String),\n `College_Location` Nullable(String),\n `college_description` Nullable(String),\n `college_description_embedding` Array(Float32)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Country` Nullable(String),\n `College_ID` Nullable(Int64),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE round (\n `Round_ID` Nullable(Int64),\n `Member_ID` Nullable(Int64),\n `Decoration_Theme` Nullable(String),\n `Rank_in_Round` Nullable(Int64),\n `Decoration_Theme_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the names of the top 3 colleges renowned as educational institutions in California, the names of the top 3 esteemed professors specializing in quantum physics affiliated with these colleges, and the themes of the top 3 futuristic technology symposiums they may be involved in. Ensure the results are ordered by the college names, member names, and decoration themes.\n\nLet's think step by step!\n" + }, + { + "db_id": "sports_competition", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A club from the UK established in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance \nFROM club\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the ID of the club that most closely fits the description of being from the UK and established in the late 1990s?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A UK-based club founded in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Club originating from the United Kingdom in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A club established in the UK during the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'British club founded in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Club from the United Kingdom set up in the late 1990s.') AS ref_vec_0\n\nSELECT Club_ID, distance(club.club_description_embedding, ref_vec_0) AS distance FROM club\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(String),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE club_rank (\n `Rank` Nullable(Float64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Float64),\n `Silver` Nullable(Float64),\n `Bronze` Nullable(Float64),\n `Total` Nullable(Float64)\n);\nCREATE TABLE competition (\n `Competition_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Competition_type` Nullable(String),\n `Country` Nullable(String),\n `competition_description` Nullable(String),\n `competition_description_embedding` Array(Float32)\n);\nCREATE TABLE competition_result (\n `Competition_ID` Nullable(Int64),\n `Club_ID_1` Nullable(Int64),\n `Club_ID_2` Nullable(Int64),\n `Score` Nullable(String)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `name` Nullable(String),\n `Position` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Apps` Nullable(Float64),\n `Tries` Nullable(Float64),\n `Goals` Nullable(String),\n `Points` Nullable(Float64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(String),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE club_rank (\n `Rank` Nullable(Float64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Float64),\n `Silver` Nullable(Float64),\n `Bronze` Nullable(Float64),\n `Total` Nullable(Float64)\n);\nCREATE TABLE competition (\n `Competition_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Competition_type` Nullable(String),\n `Country` Nullable(String),\n `competition_description` Nullable(String),\n `competition_description_embedding` Array(Float32)\n);\nCREATE TABLE competition_result (\n `Competition_ID` Nullable(Int64),\n `Club_ID_1` Nullable(Int64),\n `Club_ID_2` Nullable(Int64),\n `Score` Nullable(String)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `name` Nullable(String),\n `Position` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Apps` Nullable(Float64),\n `Tries` Nullable(Float64),\n `Goals` Nullable(String),\n `Points` Nullable(Float64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the ID of the club that most closely fits the description of being from the UK and established in the late 1990s?\n\nLet's think step by step!\n" + }, + { + "db_id": "battle_death", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Battle of Pliska ended in a decisive Bulgarian victory against Emperor Nikephoros I in 811.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance\nFROM battle\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a handful of battles that echo the essence of the Battle of Pliska's decisive outcome for the Bulgarians?", + "external_knowledge": "The query utilizes vector search capabilities to perform a semantic comparison between text descriptions. The `MATCH` operator is used to find vectors that are closest in semantic space to the provided embedding, essentially looking for similar descriptions. The `k = 5` indicates that the query is limited to retrieving the top 5 most similar items. In this context, the similarity is not based on exact text matching but on the meaning, as captured by the vector representation using the `all-MiniLM-L6-v2` model. The closer the vectors are in this space, the more semantically similar they are considered.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'The Battle of Pliska marked a significant victory for the Bulgarians, defeating Emperor Nikephoros I decisively in 811.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In 811, the Battle of Pliska resulted in a crucial win for Bulgaria, overcoming Emperor Nikephoros I.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The decisive Bulgarian triumph at the Battle of Pliska against Nikephoros I in 811.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Bulgaria''''s pivotal victory at Pliska in 811, defeating Emperor Nikephoros I.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Battle of Pliska in 811 was a defining moment for Bulgaria, with a decisive victory over Emperor Nikephoros I.') AS ref_vec_0\n\nSELECT id, name, distance(battle.battle_description_embedding, ref_vec_0) AS distance FROM battle\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE battle (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `date` Nullable(String),\n `bulgarian_commander` Nullable(String),\n `latin_commander` Nullable(String),\n `result` Nullable(String),\n `battle_description` Nullable(String),\n `result_embedding` Array(Float32),\n `battle_description_embedding` Array(Float32)\n);\nCREATE TABLE death (\n `caused_by_ship_id` Nullable(Int64),\n `id` Nullable(Int64),\n `note` Nullable(String),\n `killed` Nullable(Int64),\n `injured` Nullable(Int64),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE ship (\n `lost_in_battle` Nullable(Int64),\n `id` Nullable(Int64),\n `name` Nullable(String),\n `tonnage` Nullable(String),\n `ship_type` Nullable(String),\n `location` Nullable(String),\n `disposition_of_ship` Nullable(String),\n `ship_description` Nullable(String),\n `disposition_of_ship_embedding` Array(Float32),\n `ship_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE battle (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `date` Nullable(String),\n `bulgarian_commander` Nullable(String),\n `latin_commander` Nullable(String),\n `result` Nullable(String),\n `battle_description` Nullable(String),\n `result_embedding` Array(Float32),\n `battle_description_embedding` Array(Float32)\n);\nCREATE TABLE death (\n `caused_by_ship_id` Nullable(Int64),\n `id` Nullable(Int64),\n `note` Nullable(String),\n `killed` Nullable(Int64),\n `injured` Nullable(Int64),\n `note_embedding` Array(Float32)\n);\nCREATE TABLE ship (\n `lost_in_battle` Nullable(Int64),\n `id` Nullable(Int64),\n `name` Nullable(String),\n `tonnage` Nullable(String),\n `ship_type` Nullable(String),\n `location` Nullable(String),\n `disposition_of_ship` Nullable(String),\n `ship_description` Nullable(String),\n `disposition_of_ship_embedding` Array(Float32),\n `ship_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe query utilizes vector search capabilities to perform a semantic comparison between text descriptions. The `MATCH` operator is used to find vectors that are closest in semantic space to the provided embedding, essentially looking for similar descriptions. The `k = 5` indicates that the query is limited to retrieving the top 5 most similar items. In this context, the similarity is not based on exact text matching but on the meaning, as captured by the vector representation using the `all-MiniLM-L6-v2` model. The closer the vectors are in this space, the more semantically similar they are considered.\nCan you find a handful of battles that echo the essence of the Battle of Pliska's decisive outcome for the Bulgarians?\n\nLet's think step by step!\n" + }, + { + "db_id": "employee_hire_evaluation", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A young professional from a major city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance\nFROM employee\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Can you find me the Employee ID of the top young professional who is from a major city? I'm really interested in knowing who fits this description!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A promising young talent based in a metropolitan area') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An emerging professional from a big city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A young expert residing in a major urban center') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A youthful professional located in a large city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top young worker hailing from a significant city') AS ref_vec_0\n\nSELECT Employee_ID, distance(employee.employee_description_embedding, ref_vec_0) AS distance FROM employee\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE employee (\n `Employee_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `City` Nullable(String),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE evaluation (\n `Employee_ID` Nullable(String),\n `Year_awarded` Nullable(String),\n `Bonus` Nullable(Float64)\n);\nCREATE TABLE hiring (\n `Shop_ID` Nullable(Int64),\n `Employee_ID` Nullable(Int64),\n `Start_from` Nullable(String),\n `Is_full_time` Nullable(String)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Location` Nullable(String),\n `District` Nullable(String),\n `Number_products` Nullable(Int64),\n `Manager_name` Nullable(String),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE employee (\n `Employee_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Age` Nullable(Int64),\n `City` Nullable(String),\n `employee_description` Nullable(String),\n `employee_description_embedding` Array(Float32)\n);\nCREATE TABLE evaluation (\n `Employee_ID` Nullable(String),\n `Year_awarded` Nullable(String),\n `Bonus` Nullable(Float64)\n);\nCREATE TABLE hiring (\n `Shop_ID` Nullable(Int64),\n `Employee_ID` Nullable(Int64),\n `Start_from` Nullable(String),\n `Is_full_time` Nullable(String)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Location` Nullable(String),\n `District` Nullable(String),\n `Number_products` Nullable(Int64),\n `Manager_name` Nullable(String),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCan you find me the Employee ID of the top young professional who is from a major city? I'm really interested in knowing who fits this description!\n\nLet's think step by step!\n" + }, + { + "db_id": "solvency_ii", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A conference discussing advancements in AI technology.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance\nFROM Events e\nJOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few events about AI technology and provide their IDs and details of where they are held?", + "external_knowledge": "In vector search operations, the `MATCH` operator in conjunction with the `lembed` function performs an approximate nearest neighbor (ANN) search. This search is typically used to find items that are semantically similar based on vector embeddings. The `k=3` parameter specifies that the search should return the top 3 most relevant results. The embeddings are compared using the Euclidean distance (L2 norm), where a smaller distance indicates higher similarity. In this context, the query aims to find events that are closely related to the theme of AI technology advancements, and it assumes that \"a few\" refers to the top 3 similar events.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Events focused on AI technology developments.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Meetings discussing the future of AI innovations.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Gatherings about advancements in artificial intelligence.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Seminars on AI technology progress.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Discussions on the latest AI tech trends.') AS ref_vec_0\n\nSELECT e.Event_ID, l.Other_Details, distance(e.Events_description_embedding, ref_vec_0) AS distance FROM Events e JOIN Locations l ON toString(e.Location_ID) = toString(l.Location_ID)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `Address_ID` Int64,\n `address_details` Nullable(String)\n);\nCREATE TABLE Agreements (\n `Document_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets (\n `Asset_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Assets_in_Events (\n `Asset_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Channels (\n `Channel_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Events (\n `Event_ID` Nullable(Int64),\n `Address_ID` Nullable(Int64),\n `Channel_ID` Nullable(Int64),\n `Event_Type_Code` Nullable(String),\n `Finance_ID` Nullable(Int64),\n `Location_ID` Nullable(Int64),\n `Events_description` Nullable(String),\n `Events_description_embedding` Array(Float32)\n);\nCREATE TABLE Events_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Finances (\n `Finance_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Locations (\n `Location_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Locations_description` Nullable(String),\n `Locations_description_embedding` Array(Float32)\n);\nCREATE TABLE Locations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Parties (\n `Party_ID` Nullable(Int64),\n `Party_Details` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties_in_Events (\n `Party_ID` Int64,\n `Event_ID` Int64,\n `Role_Code` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Products (\n `Product_ID` Nullable(Int64),\n `Product_Type_Code` Nullable(String),\n `Product_Name` Nullable(String),\n `Product_Price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_in_Events (\n `Product_in_Event_ID` Int64,\n `Event_ID` Int64,\n `Product_ID` Int64\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `Address_ID` Int64,\n `address_details` Nullable(String)\n);\nCREATE TABLE Agreements (\n `Document_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets (\n `Asset_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Assets_description` Nullable(String),\n `Assets_description_embedding` Array(Float32)\n);\nCREATE TABLE Assets_in_Events (\n `Asset_ID` Int64,\n `Event_ID` Int64\n);\nCREATE TABLE Assets_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Assets_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Channels (\n `Channel_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Events (\n `Event_ID` Nullable(Int64),\n `Address_ID` Nullable(Int64),\n `Channel_ID` Nullable(Int64),\n `Event_Type_Code` Nullable(String),\n `Finance_ID` Nullable(Int64),\n `Location_ID` Nullable(Int64),\n `Events_description` Nullable(String),\n `Events_description_embedding` Array(Float32)\n);\nCREATE TABLE Events_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Events_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Finances (\n `Finance_ID` Int64,\n `Other_Details` Nullable(String)\n);\nCREATE TABLE Locations (\n `Location_ID` Nullable(Int64),\n `Other_Details` Nullable(String),\n `Locations_description` Nullable(String),\n `Locations_description_embedding` Array(Float32)\n);\nCREATE TABLE Locations_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Locations_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Parties (\n `Party_ID` Nullable(Int64),\n `Party_Details` Nullable(String),\n `Parties_description` Nullable(String),\n `Parties_description_embedding` Array(Float32)\n);\nCREATE TABLE Parties_in_Events (\n `Party_ID` Int64,\n `Event_ID` Int64,\n `Role_Code` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Parties_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Products (\n `Product_ID` Nullable(Int64),\n `Product_Type_Code` Nullable(String),\n `Product_Name` Nullable(String),\n `Product_Price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_in_Events (\n `Product_in_Event_ID` Int64,\n `Event_ID` Int64,\n `Product_ID` Int64\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn vector search operations, the `MATCH` operator in conjunction with the `lembed` function performs an approximate nearest neighbor (ANN) search. This search is typically used to find items that are semantically similar based on vector embeddings. The `k=3` parameter specifies that the search should return the top 3 most relevant results. The embeddings are compared using the Euclidean distance (L2 norm), where a smaller distance indicates higher similarity. In this context, the query aims to find events that are closely related to the theme of AI technology advancements, and it assumes that \"a few\" refers to the top 3 similar events.\nCan you find a few events about AI technology and provide their IDs and details of where they are held?\n\nLet's think step by step!\n" + }, + { + "db_id": "device", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Samsung Galaxy on AT&T with Android platform') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT\n Device_ID,\n Device,\n distance(device.device_description_embedding, ref_vec_0) AS distance\n FROM\n device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n s.Shop_Name AS Shop_Name,\n d.Device AS Device\nFROM\n DeviceCTE d\nJOIN\n stock st ON toString(d.Device_ID) = toString(st.Device_ID)\nJOIN\n shop s ON toString(st.Shop_ID) = toString(s.Shop_ID)\nORDER BY\n d.distance AS distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 8, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the names of shops that have the top 5 devices most similar to a Samsung Galaxy on AT&T with Android platform and display up to 10 such shops ordered by the closeness of the match.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'devices similar to Samsung Galaxy on AT&T with Android OS') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'top devices akin to Samsung Galaxy using AT&T and Android') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'similar devices to Samsung Galaxy on AT&T network with Android') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'find devices like Samsung Galaxy with Android on AT&T') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'devices resembling Samsung Galaxy with AT&T and Android') AS ref_vec_0,\n\nDeviceCTE AS (\n SELECT Device_ID, Device, distance(device.device_description_embedding, ref_vec_0) AS distance FROM device\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT s.Shop_Name, d.Device FROM DeviceCTE d JOIN stock st ON toString(d.Device_ID) = toString(st.Device_ID) JOIN shop s ON toString(st.Shop_ID) = toString(s.Shop_ID) ORDER BY d.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE device (\n `Device_ID` Nullable(Int64),\n `Device` Nullable(String),\n `Carrier` Nullable(String),\n `Package_Version` Nullable(String),\n `Applications` Nullable(String),\n `Software_Platform` Nullable(String),\n `device_description` Nullable(String),\n `device_description_embedding` Array(Float32)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Shop_Name` Nullable(String),\n `Location` Nullable(String),\n `Open_Date` Nullable(String),\n `Open_Year` Nullable(Int64),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);\nCREATE TABLE stock (\n `Shop_ID` Nullable(Int64),\n `Device_ID` Nullable(Int64),\n `Quantity` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE device (\n `Device_ID` Nullable(Int64),\n `Device` Nullable(String),\n `Carrier` Nullable(String),\n `Package_Version` Nullable(String),\n `Applications` Nullable(String),\n `Software_Platform` Nullable(String),\n `device_description` Nullable(String),\n `device_description_embedding` Array(Float32)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Shop_Name` Nullable(String),\n `Location` Nullable(String),\n `Open_Date` Nullable(String),\n `Open_Year` Nullable(Int64),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);\nCREATE TABLE stock (\n `Shop_ID` Nullable(Int64),\n `Device_ID` Nullable(Int64),\n `Quantity` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the names of shops that have the top 5 devices most similar to a Samsung Galaxy on AT&T with Android platform and display up to 10 such shops ordered by the closeness of the match.\n\nLet's think step by step!\n" + }, + { + "db_id": "music_4", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An artist known for a groundbreaking album released in the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT \n Artist_ID,\n Artist,\n Famous_Title,\n Famous_Release_date,\n distance(artist.artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.Famous_Title AS Famous_Title\nFROM ArtistKNN a\nJOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID)\nWHERE v.Weeks_on_Top > 2\nORDER BY a.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I am looking for the most notable album title by an artist who had a significant groundbreaking release in the early 2000s. The album should have been on the top charts for more than 2 weeks. Could you provide the title of this album?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An artist who made waves in the early 2000s with a pivotal album release.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Artist with a landmark album from the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Known for a notable album that defined the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An influential artist with a top album from the early 2000s.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Artist famous for a revolutionary early 2000s album.') AS ref_vec_0,\n\nArtistKNN AS (\n SELECT Artist_ID, Artist, Famous_Title, Famous_Release_date, distance(artist.artist_description_embedding, ref_vec_0) AS distance FROM artist\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.Famous_Title FROM ArtistKNN a JOIN volume v ON toString(a.Artist_ID) = toString(v.Artist_ID) WHERE v.Weeks_on_Top > 2 ORDER BY a.distance LIMIT 1;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Artist` Nullable(String),\n `Age` Nullable(Int64),\n `Famous_Title` Nullable(String),\n `Famous_Release_date` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE music_festival (\n `ID` Nullable(Int64),\n `Music_Festival` Nullable(String),\n `Date_of_ceremony` Nullable(String),\n `Category` Nullable(String),\n `Volume` Nullable(Int64),\n `Result` Nullable(String),\n `music_festival_description` Nullable(String),\n `music_festival_description_embedding` Array(Float32)\n);\nCREATE TABLE volume (\n `Volume_ID` Nullable(Int64),\n `Volume_Issue` Nullable(String),\n `Issue_Date` Nullable(String),\n `Weeks_on_Top` Nullable(Float64),\n `Song` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `volume_description` Nullable(String),\n `volume_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Artist` Nullable(String),\n `Age` Nullable(Int64),\n `Famous_Title` Nullable(String),\n `Famous_Release_date` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE music_festival (\n `ID` Nullable(Int64),\n `Music_Festival` Nullable(String),\n `Date_of_ceremony` Nullable(String),\n `Category` Nullable(String),\n `Volume` Nullable(Int64),\n `Result` Nullable(String),\n `music_festival_description` Nullable(String),\n `music_festival_description_embedding` Array(Float32)\n);\nCREATE TABLE volume (\n `Volume_ID` Nullable(Int64),\n `Volume_Issue` Nullable(String),\n `Issue_Date` Nullable(String),\n `Weeks_on_Top` Nullable(Float64),\n `Song` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `volume_description` Nullable(String),\n `volume_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI am looking for the most notable album title by an artist who had a significant groundbreaking release in the early 2000s. The album should have been on the top charts for more than 2 weeks. Could you provide the title of this album?\n\nLet's think step by step!\n" + }, + { + "db_id": "music_4", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A contemporary artist known for their innovative music') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A top-charting song released recently') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT \n a.Artist_ID AS Artist_ID, \n a.Artist AS Artist, \n v.Volume_ID AS Volume_ID, \n v.Song AS Song,\n v.Weeks_on_Top AS Weeks_on_Top,\n v.distance AS vol_distance\n FROM a_filtered AS a\n JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT \n Artist_ID, \n Volume_ID\nFROM\n ArtistVolumeCTE\nORDER BY \n vol_distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 4, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Venture into the musical realm and unearth the IDs of artists and their songs that resonate with the essence of contemporary innovation in music and have recently topped the charts.", + "external_knowledge": "In SQLite with extensions like sqlite-vec and sqlite-lembed, vector searches are employed to find entries based on semantic similarity. The `MATCH` operator performs an approximate nearest neighbor (ANN) search using embeddings generated by models like 'all-MiniLM-L6-v2'. The parameter `k=N` specifies the retrieval of the top N most similar items. Euclidean distance is commonly used to measure similarity, where smaller distances indicate higher similarity. In this context, \"A contemporary artist known for their innovative music\" suggests modern artists pushing musical boundaries, while \"A top-charting song released recently\" signifies songs currently receiving significant attention and acclaim.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An innovative musician shaping modern music trends') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A recent chart-topping hit') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A trailblazing artist in the modern music scene') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A song that recently dominated the charts') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A musician pushing the boundaries of contemporary music') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A song that has recently topped the music charts') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A contemporary artist revolutionizing music innovation') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A recent song that led the charts') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An artist at the forefront of modern musical innovation') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A song that has recently been a chart leader') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(artist_description_embedding, ref_vec_0) AS distance\n FROM artist\n\n ORDER BY distance\n LIMIT 5\n),\n\nv_filtered AS (\n SELECT\n *,\n distance(volume_description_embedding, ref_vec_1) AS distance\n FROM volume\n\n ORDER BY distance\n LIMIT 5\n),\n\nArtistVolumeCTE AS (\n SELECT a.Artist_ID, a.Artist, v.Volume_ID, v.Song, v.Weeks_on_Top, v.distance AS vol_distance FROM a_filtered AS a JOIN v_filtered AS v ON toString(a.Artist_ID) = toString(v.Artist_ID)\n)\n\nSELECT Artist_ID, Volume_ID FROM ArtistVolumeCTE ORDER BY vol_distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Artist` Nullable(String),\n `Age` Nullable(Int64),\n `Famous_Title` Nullable(String),\n `Famous_Release_date` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE music_festival (\n `ID` Nullable(Int64),\n `Music_Festival` Nullable(String),\n `Date_of_ceremony` Nullable(String),\n `Category` Nullable(String),\n `Volume` Nullable(Int64),\n `Result` Nullable(String),\n `music_festival_description` Nullable(String),\n `music_festival_description_embedding` Array(Float32)\n);\nCREATE TABLE volume (\n `Volume_ID` Nullable(Int64),\n `Volume_Issue` Nullable(String),\n `Issue_Date` Nullable(String),\n `Weeks_on_Top` Nullable(Float64),\n `Song` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `volume_description` Nullable(String),\n `volume_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE artist (\n `Artist_ID` Nullable(Int64),\n `Artist` Nullable(String),\n `Age` Nullable(Int64),\n `Famous_Title` Nullable(String),\n `Famous_Release_date` Nullable(String),\n `artist_description` Nullable(String),\n `artist_description_embedding` Array(Float32)\n);\nCREATE TABLE music_festival (\n `ID` Nullable(Int64),\n `Music_Festival` Nullable(String),\n `Date_of_ceremony` Nullable(String),\n `Category` Nullable(String),\n `Volume` Nullable(Int64),\n `Result` Nullable(String),\n `music_festival_description` Nullable(String),\n `music_festival_description_embedding` Array(Float32)\n);\nCREATE TABLE volume (\n `Volume_ID` Nullable(Int64),\n `Volume_Issue` Nullable(String),\n `Issue_Date` Nullable(String),\n `Weeks_on_Top` Nullable(Float64),\n `Song` Nullable(String),\n `Artist_ID` Nullable(Int64),\n `volume_description` Nullable(String),\n `volume_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn SQLite with extensions like sqlite-vec and sqlite-lembed, vector searches are employed to find entries based on semantic similarity. The `MATCH` operator performs an approximate nearest neighbor (ANN) search using embeddings generated by models like 'all-MiniLM-L6-v2'. The parameter `k=N` specifies the retrieval of the top N most similar items. Euclidean distance is commonly used to measure similarity, where smaller distances indicate higher similarity. In this context, \"A contemporary artist known for their innovative music\" suggests modern artists pushing musical boundaries, while \"A top-charting song released recently\" signifies songs currently receiving significant attention and acclaim.\nVenture into the musical realm and unearth the IDs of artists and their songs that resonate with the essence of contemporary innovation in music and have recently topped the charts.\n\nLet's think step by step!\n" + }, + { + "db_id": "riding_club", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A female athlete from Vancouver with impressive stats and a strong fan base.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance\nFROM player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which player is a female athlete from Vancouver with impressive stats and a strong fan base?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A female sports star from Vancouver known for her remarkable performance and large fan following.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A woman athlete hailing from Vancouver with outstanding stats and a significant number of fans.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent female player from Vancouver with excellent statistics and a devoted fan base.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A female competitor from Vancouver with impressive records and a strong following.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known female athlete from Vancouver with great stats and a loyal fan community.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `Club_name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(Int64),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE coach (\n `Coach_ID` Nullable(Int64),\n `Coach_name` Nullable(String),\n `Gender` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Rank` Nullable(Int64),\n `coach_description` Nullable(String),\n `coach_description_embedding` Array(Float32)\n);\nCREATE TABLE match_result (\n `Rank` Nullable(Int64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Int64),\n `Big_Silver` Nullable(Int64),\n `Small_Silver` Nullable(Int64),\n `Bronze` Nullable(Int64),\n `Points` Nullable(Int64)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Sponsor_name` Nullable(String),\n `Player_name` Nullable(String),\n `Gender` Nullable(String),\n `Residence` Nullable(String),\n `Occupation` Nullable(String),\n `Votes` Nullable(Int64),\n `Rank` Nullable(String),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE player_coach (\n `Player_ID` Nullable(Int64),\n `Coach_ID` Nullable(Int64),\n `Starting_year` Nullable(Int64)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE club (\n `Club_ID` Nullable(Int64),\n `Club_name` Nullable(String),\n `Region` Nullable(String),\n `Start_year` Nullable(Int64),\n `club_description` Nullable(String),\n `club_description_embedding` Array(Float32)\n);\nCREATE TABLE coach (\n `Coach_ID` Nullable(Int64),\n `Coach_name` Nullable(String),\n `Gender` Nullable(String),\n `Club_ID` Nullable(Int64),\n `Rank` Nullable(Int64),\n `coach_description` Nullable(String),\n `coach_description_embedding` Array(Float32)\n);\nCREATE TABLE match_result (\n `Rank` Nullable(Int64),\n `Club_ID` Nullable(Int64),\n `Gold` Nullable(Int64),\n `Big_Silver` Nullable(Int64),\n `Small_Silver` Nullable(Int64),\n `Bronze` Nullable(Int64),\n `Points` Nullable(Int64)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Sponsor_name` Nullable(String),\n `Player_name` Nullable(String),\n `Gender` Nullable(String),\n `Residence` Nullable(String),\n `Occupation` Nullable(String),\n `Votes` Nullable(Int64),\n `Rank` Nullable(String),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE player_coach (\n `Player_ID` Nullable(Int64),\n `Coach_ID` Nullable(Int64),\n `Starting_year` Nullable(Int64)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me which player is a female athlete from Vancouver with impressive stats and a strong fan base?\n\nLet's think step by step!\n" + }, + { + "db_id": "customers_card_transactions", + "sql": "WITH ActiveCards AS (\n SELECT \n card_id,\n customer_id\n FROM \n Customers_Cards\n WHERE \n date_valid_to > now()\n),\n\n\nRecentTransactions AS (\n SELECT \n ft.transaction_id AS transaction_id,\n ft.account_id AS account_id,\n ft.card_id AS card_id,\n ft.transaction_date AS transaction_date,\n ft.transaction_amount AS transaction_amount,\n ROW_NUMBER() OVER (PARTITION BY ft.card_id ORDER BY ft.transaction_date DESC) AS rn\n FROM \n Financial_Transactions ft\n JOIN \n ActiveCards ac ON toString(ft.card_id) = toString(ac.card_id)\n),\n\n\nCustomerAccounts AS (\n SELECT\n c.customer_id AS customer_id,\n c.customer_first_name || ' ' || c.customer_last_name AS full_name,\n a.account_id AS account_id,\n a.account_name AS account_name\n FROM \n Customers c\n JOIN \n Accounts a ON toString(c.customer_id) = toString(a.customer_id)\n)\n\n\nSELECT \n ca.full_name AS full_name,\n rt.transaction_amount AS transaction_amount\nFROM \n RecentTransactions rt\nJOIN \n CustomerAccounts ca ON toString(rt.account_id) = toString(ca.account_id)\nWHERE \n rt.rn = 1\nORDER BY \n ca.full_name;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Retrieve the full names of customers and the amounts of their most recent transactions from active cards, ordered alphabetically by the customers' full names.", + "external_knowledge": "", + "sql_candidate": [ + "WITH ActiveCards AS (\n SELECT \n card_id,\n customer_id\n FROM \n Customers_Cards\n WHERE \n date_valid_to > now()\n),\n\n\nRecentTransactions AS (\n SELECT \n ft.transaction_id AS transaction_id,\n ft.account_id AS account_id,\n ft.card_id AS card_id,\n ft.transaction_date AS transaction_date,\n ft.transaction_amount AS transaction_amount,\n ROW_NUMBER() OVER (PARTITION BY ft.card_id ORDER BY ft.transaction_date DESC) AS rn\n FROM \n Financial_Transactions ft\n JOIN \n ActiveCards ac ON toString(ft.card_id) = toString(ac.card_id)\n),\n\n\nCustomerAccounts AS (\n SELECT\n c.customer_id AS customer_id,\n c.customer_first_name || ' ' || c.customer_last_name AS full_name,\n a.account_id AS account_id,\n a.account_name AS account_name\n FROM \n Customers c\n JOIN \n Accounts a ON toString(c.customer_id) = toString(a.customer_id)\n)\n\n\nSELECT \n ca.full_name AS full_name,\n rt.transaction_amount AS transaction_amount\nFROM \n RecentTransactions rt\nJOIN \n CustomerAccounts ca ON toString(rt.account_id) = toString(ca.account_id)\nWHERE \n rt.rn = 1\nORDER BY \n ca.full_name;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Accounts (\n `account_id` Nullable(Int64),\n `customer_id` Int64,\n `account_name` Nullable(String),\n `other_account_details` Nullable(String),\n `Accounts_description` Nullable(String)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_first_name` Nullable(String),\n `customer_last_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `other_customer_details` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Customers_Cards (\n `card_id` Nullable(Int64),\n `customer_id` Int64,\n `card_type_code` String,\n `card_number` Nullable(String),\n `date_valid_from` Nullable(Date),\n `date_valid_to` Nullable(Date),\n `other_card_details` Nullable(String)\n);\nCREATE TABLE Financial_Transactions (\n `transaction_id` Int64,\n `previous_transaction_id` Nullable(Int64),\n `account_id` Int64,\n `card_id` Int64,\n `transaction_type` String,\n `transaction_date` Nullable(Date),\n `transaction_amount` Nullable(Float64),\n `transaction_comment` Nullable(String),\n `other_transaction_details` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Accounts (\n `account_id` Nullable(Int64),\n `customer_id` Int64,\n `account_name` Nullable(String),\n `other_account_details` Nullable(String),\n `Accounts_description` Nullable(String)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_first_name` Nullable(String),\n `customer_last_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `other_customer_details` Nullable(String),\n `Customers_description` Nullable(String)\n);\nCREATE TABLE Customers_Cards (\n `card_id` Nullable(Int64),\n `customer_id` Int64,\n `card_type_code` String,\n `card_number` Nullable(String),\n `date_valid_from` Nullable(Date),\n `date_valid_to` Nullable(Date),\n `other_card_details` Nullable(String)\n);\nCREATE TABLE Financial_Transactions (\n `transaction_id` Int64,\n `previous_transaction_id` Nullable(Int64),\n `account_id` Int64,\n `card_id` Int64,\n `transaction_type` String,\n `transaction_date` Nullable(Date),\n `transaction_amount` Nullable(Float64),\n `transaction_comment` Nullable(String),\n `other_transaction_details` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nRetrieve the full names of customers and the amounts of their most recent transactions from active cards, ordered alphabetically by the customers' full names.\n\nLet's think step by step!\n" + }, + { + "db_id": "phone_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A high performance chip model with advanced capabilities') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance \nFROM chip_model\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "** \nCould you help me find one chip model that is known for its high performance and advanced capabilities? I really need its name and the year it was launched! \n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A chip model renowned for its superior performance and cutting-edge features') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A high-performance chip model with state-of-the-art capabilities') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An advanced chip model known for exceptional performance') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top-tier chip model with impressive performance and features') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A leading chip model recognized for high performance and advanced technology') AS ref_vec_0\n\nSELECT Model_name, Launch_year, distance(chip_model.chip_model_description_embedding, ref_vec_0) AS distance FROM chip_model\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE chip_model (\n `Model_name` Nullable(String),\n `Launch_year` Nullable(Float64),\n `RAM_MiB` Nullable(Float64),\n `ROM_MiB` Nullable(Float64),\n `Slots` Nullable(String),\n `WiFi` Nullable(String),\n `Bluetooth` Nullable(String),\n `chip_model_description` Nullable(String),\n `chip_model_description_embedding` Array(Float32)\n);\nCREATE TABLE chip_model_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE phone (\n `Company_name` Nullable(String),\n `Hardware_Model_name` Nullable(String),\n `Accreditation_type` Nullable(String),\n `Accreditation_level` Nullable(String),\n `Date` Nullable(String),\n `chip_model` Nullable(String),\n `screen_mode` Nullable(String),\n `phone_description` Nullable(String),\n `phone_description_embedding` Array(Float32)\n);\nCREATE TABLE phone_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE screen_mode (\n `Graphics_mode` Nullable(Float64),\n `Char_cells` Nullable(String),\n `Pixels` Nullable(String),\n `Hardware_colours` Nullable(Float64),\n `used_kb` Nullable(Float64),\n `map` Nullable(String),\n `Type` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE chip_model (\n `Model_name` Nullable(String),\n `Launch_year` Nullable(Float64),\n `RAM_MiB` Nullable(Float64),\n `ROM_MiB` Nullable(Float64),\n `Slots` Nullable(String),\n `WiFi` Nullable(String),\n `Bluetooth` Nullable(String),\n `chip_model_description` Nullable(String),\n `chip_model_description_embedding` Array(Float32)\n);\nCREATE TABLE chip_model_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE chip_model_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE phone (\n `Company_name` Nullable(String),\n `Hardware_Model_name` Nullable(String),\n `Accreditation_type` Nullable(String),\n `Accreditation_level` Nullable(String),\n `Date` Nullable(String),\n `chip_model` Nullable(String),\n `screen_mode` Nullable(String),\n `phone_description` Nullable(String),\n `phone_description_embedding` Array(Float32)\n);\nCREATE TABLE phone_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE phone_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE screen_mode (\n `Graphics_mode` Nullable(Float64),\n `Char_cells` Nullable(String),\n `Pixels` Nullable(String),\n `Hardware_colours` Nullable(Float64),\n `used_kb` Nullable(Float64),\n `map` Nullable(String),\n `Type` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n** \nCould you help me find one chip model that is known for its high performance and advanced capabilities? I really need its name and the year it was launched! \n**\n\nLet's think step by step!\n" + }, + { + "db_id": "sakila_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling adventure of a young hero in a futuristic world') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance\nFROM film\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you find me the film ID for a movie that best represents a thrilling adventure of a young hero in a futuristic world?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An exciting journey of a young protagonist in a futuristic setting') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A young hero''''s thrilling quest in a sci-fi world') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A daring adventure of a youthful hero in a future society') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A suspenseful journey of a young hero in a high-tech universe') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling expedition of a young hero in an advanced world') AS ref_vec_0\n\nSELECT film_id, distance(film.description_embedding, ref_vec_0) AS distance FROM film\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `actor_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `last_update` Nullable(String),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE address (\n `address_id` Nullable(Int64),\n `address` Nullable(String),\n `address2` Nullable(String),\n `district` Nullable(String),\n `city_id` Nullable(Int64),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `last_update` Nullable(String),\n `address_description` Nullable(String),\n `address_description_embedding` Array(Float32)\n);\nCREATE TABLE category (\n `category_id` Nullable(Int64),\n `name` Nullable(String),\n `last_update` Nullable(String),\n `category_description` Nullable(String),\n `category_description_embedding` Array(Float32)\n);\nCREATE TABLE city (\n `city_id` Nullable(Int64),\n `city` Nullable(String),\n `country_id` Nullable(Int64),\n `last_update` Nullable(String),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE country (\n `country_id` Nullable(Int64),\n `country` Nullable(String),\n `last_update` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `customer_id` Nullable(Int64),\n `store_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `address_id` Nullable(Int64),\n `active` Nullable(String),\n `create_date` Nullable(String),\n `last_update` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE film (\n `film_id` Nullable(Int64),\n `title` Nullable(String),\n `description` Nullable(String),\n `release_year` Nullable(String),\n `language_id` Nullable(Int64),\n `original_language_id` Nullable(Int64),\n `rental_duration` Nullable(Int64),\n `rental_rate` Nullable(Float64),\n `length` Nullable(Int64),\n `replacement_cost` Nullable(Float64),\n `rating` Nullable(String),\n `special_features` Nullable(String),\n `last_update` Nullable(String),\n `title_embedding` Array(Float32),\n `description_embedding` Array(Float32)\n);\nCREATE TABLE film_actor (\n `actor_id` Int64,\n `film_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_category (\n `film_id` Int64,\n `category_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_text (\n `film_id` Int64,\n `title` String,\n `description` Nullable(String)\n);\nCREATE TABLE inventory (\n `inventory_id` Int64,\n `film_id` Int64,\n `store_id` Int64,\n `last_update` String\n);\nCREATE TABLE language (\n `language_id` Int64,\n `name` String,\n `last_update` String\n);\nCREATE TABLE payment (\n `payment_id` Int64,\n `customer_id` Int64,\n `staff_id` Int64,\n `rental_id` Nullable(Int64),\n `amount` Decimal(38, 6),\n `payment_date` Date,\n `last_update` Nullable(String)\n);\nCREATE TABLE rental (\n `rental_id` Int64,\n `rental_date` Date,\n `inventory_id` Int64,\n `customer_id` Int64,\n `return_date` Nullable(Date),\n `staff_id` Int64,\n `last_update` String\n);\nCREATE TABLE staff (\n `staff_id` Int64,\n `first_name` String,\n `last_name` String,\n `address_id` Int64,\n `picture` Nullable(String),\n `email` Nullable(String),\n `store_id` Int64,\n `active` String,\n `username` String,\n `password` Nullable(String),\n `last_update` String,\n `staff_description` Nullable(String)\n);\nCREATE TABLE store (\n `store_id` Int64,\n `manager_staff_id` Int64,\n `address_id` Int64,\n `last_update` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE actor (\n `actor_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `last_update` Nullable(String),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE address (\n `address_id` Nullable(Int64),\n `address` Nullable(String),\n `address2` Nullable(String),\n `district` Nullable(String),\n `city_id` Nullable(Int64),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `last_update` Nullable(String),\n `address_description` Nullable(String),\n `address_description_embedding` Array(Float32)\n);\nCREATE TABLE category (\n `category_id` Nullable(Int64),\n `name` Nullable(String),\n `last_update` Nullable(String),\n `category_description` Nullable(String),\n `category_description_embedding` Array(Float32)\n);\nCREATE TABLE city (\n `city_id` Nullable(Int64),\n `city` Nullable(String),\n `country_id` Nullable(Int64),\n `last_update` Nullable(String),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE country (\n `country_id` Nullable(Int64),\n `country` Nullable(String),\n `last_update` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `customer_id` Nullable(Int64),\n `store_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `address_id` Nullable(Int64),\n `active` Nullable(String),\n `create_date` Nullable(String),\n `last_update` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE film (\n `film_id` Nullable(Int64),\n `title` Nullable(String),\n `description` Nullable(String),\n `release_year` Nullable(String),\n `language_id` Nullable(Int64),\n `original_language_id` Nullable(Int64),\n `rental_duration` Nullable(Int64),\n `rental_rate` Nullable(Float64),\n `length` Nullable(Int64),\n `replacement_cost` Nullable(Float64),\n `rating` Nullable(String),\n `special_features` Nullable(String),\n `last_update` Nullable(String),\n `title_embedding` Array(Float32),\n `description_embedding` Array(Float32)\n);\nCREATE TABLE film_actor (\n `actor_id` Int64,\n `film_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_category (\n `film_id` Int64,\n `category_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_text (\n `film_id` Int64,\n `title` String,\n `description` Nullable(String)\n);\nCREATE TABLE inventory (\n `inventory_id` Int64,\n `film_id` Int64,\n `store_id` Int64,\n `last_update` String\n);\nCREATE TABLE language (\n `language_id` Int64,\n `name` String,\n `last_update` String\n);\nCREATE TABLE payment (\n `payment_id` Int64,\n `customer_id` Int64,\n `staff_id` Int64,\n `rental_id` Nullable(Int64),\n `amount` Decimal(38, 6),\n `payment_date` Date,\n `last_update` Nullable(String)\n);\nCREATE TABLE rental (\n `rental_id` Int64,\n `rental_date` Date,\n `inventory_id` Int64,\n `customer_id` Int64,\n `return_date` Nullable(Date),\n `staff_id` Int64,\n `last_update` String\n);\nCREATE TABLE staff (\n `staff_id` Int64,\n `first_name` String,\n `last_name` String,\n `address_id` Int64,\n `picture` Nullable(String),\n `email` Nullable(String),\n `store_id` Int64,\n `active` String,\n `username` String,\n `password` Nullable(String),\n `last_update` String,\n `staff_description` Nullable(String)\n);\nCREATE TABLE store (\n `store_id` Int64,\n `manager_staff_id` Int64,\n `address_id` Int64,\n `last_update` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you find me the film ID for a movie that best represents a thrilling adventure of a young hero in a futuristic world?\n\nLet's think step by step!\n" + }, + { + "db_id": "ship_mission", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A mission description similar to exploring the Arctic waters in the 1960s') AS ref_vec_0\n\nSELECT \n m.Mission_ID AS Mission_ID, \n s.Name AS Ship_Name, \n s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance\nFROM \n mission m\nJOIN \n ship s \nON toString(m.Ship_ID) = toString(s.Ship_ID)\nWHERE \n m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top 5 missions launched after 1950 that are most closely related to the concept of exploring Arctic waters in the 1960s. Please provide the mission IDs, along with the names and types of the ships involved in these missions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'explorations of Arctic waters during the 1960s') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'missions related to Arctic exploration in the 1960s') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '1960s Arctic waters exploration missions') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'investigating Arctic waters in the 1960s') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', '1960s missions focused on Arctic sea exploration') AS ref_vec_0\n\nSELECT m.Mission_ID, s.Name AS Ship_Name, s.Type AS Ship_Type, distance(m.mission_description_embedding, ref_vec_0) AS distance FROM mission m JOIN ship s ON toString(m.Ship_ID) = toString(s.Ship_ID) WHERE m.Launched_Year > 1950\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE mission (\n `Mission_ID` Nullable(Int64),\n `Ship_ID` Nullable(Int64),\n `Code` Nullable(String),\n `Launched_Year` Nullable(Int64),\n `Location` Nullable(String),\n `Speed_knots` Nullable(Int64),\n `Fate` Nullable(String),\n `mission_description` Nullable(String),\n `mission_description_embedding` Array(Float32)\n);\nCREATE TABLE mission_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE ship (\n `Ship_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Type` Nullable(String),\n `Nationality` Nullable(String),\n `Tonnage` Nullable(Int64),\n `ship_description` Nullable(String),\n `ship_description_embedding` Array(Float32)\n);\nCREATE TABLE ship_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE mission (\n `Mission_ID` Nullable(Int64),\n `Ship_ID` Nullable(Int64),\n `Code` Nullable(String),\n `Launched_Year` Nullable(Int64),\n `Location` Nullable(String),\n `Speed_knots` Nullable(Int64),\n `Fate` Nullable(String),\n `mission_description` Nullable(String),\n `mission_description_embedding` Array(Float32)\n);\nCREATE TABLE mission_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE ship (\n `Ship_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Type` Nullable(String),\n `Nationality` Nullable(String),\n `Tonnage` Nullable(Int64),\n `ship_description` Nullable(String),\n `ship_description_embedding` Array(Float32)\n);\nCREATE TABLE ship_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 5 missions launched after 1950 that are most closely related to the concept of exploring Arctic waters in the 1960s. Please provide the mission IDs, along with the names and types of the ships involved in these missions.\n\nLet's think step by step!\n" + }, + { + "db_id": "sakila_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Spectacular adventure film starring leading actors') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance\nFROM film f\nJOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id)\nWHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you provide the titles of the top 3 spectacular adventure films in which the actor with ID 5 has starred?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top 3 breathtaking adventure movies featuring actor with ID 5') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading adventure films with actor ID 5 in spectacular roles') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top adventure films starring actor ID 5 in amazing performances') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Spectacular adventure movies with actor ID 5') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best adventure films featuring actor ID 5 in standout roles') AS ref_vec_0\n\nSELECT f.title, fa.actor_id, distance(f.title_embedding, ref_vec_0) AS distance FROM film f JOIN film_actor fa ON toString(f.film_id) = toString(fa.film_id) WHERE fa.actor_id = 5\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE actor (\n `actor_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `last_update` Nullable(String),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE address (\n `address_id` Nullable(Int64),\n `address` Nullable(String),\n `address2` Nullable(String),\n `district` Nullable(String),\n `city_id` Nullable(Int64),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `last_update` Nullable(String),\n `address_description` Nullable(String),\n `address_description_embedding` Array(Float32)\n);\nCREATE TABLE category (\n `category_id` Nullable(Int64),\n `name` Nullable(String),\n `last_update` Nullable(String),\n `category_description` Nullable(String),\n `category_description_embedding` Array(Float32)\n);\nCREATE TABLE city (\n `city_id` Nullable(Int64),\n `city` Nullable(String),\n `country_id` Nullable(Int64),\n `last_update` Nullable(String),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE country (\n `country_id` Nullable(Int64),\n `country` Nullable(String),\n `last_update` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `customer_id` Nullable(Int64),\n `store_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `address_id` Nullable(Int64),\n `active` Nullable(String),\n `create_date` Nullable(String),\n `last_update` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE film (\n `film_id` Nullable(Int64),\n `title` Nullable(String),\n `description` Nullable(String),\n `release_year` Nullable(String),\n `language_id` Nullable(Int64),\n `original_language_id` Nullable(Int64),\n `rental_duration` Nullable(Int64),\n `rental_rate` Nullable(Float64),\n `length` Nullable(Int64),\n `replacement_cost` Nullable(Float64),\n `rating` Nullable(String),\n `special_features` Nullable(String),\n `last_update` Nullable(String),\n `title_embedding` Array(Float32),\n `description_embedding` Array(Float32)\n);\nCREATE TABLE film_actor (\n `actor_id` Int64,\n `film_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_category (\n `film_id` Int64,\n `category_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_text (\n `film_id` Int64,\n `title` String,\n `description` Nullable(String)\n);\nCREATE TABLE inventory (\n `inventory_id` Int64,\n `film_id` Int64,\n `store_id` Int64,\n `last_update` String\n);\nCREATE TABLE language (\n `language_id` Int64,\n `name` String,\n `last_update` String\n);\nCREATE TABLE payment (\n `payment_id` Int64,\n `customer_id` Int64,\n `staff_id` Int64,\n `rental_id` Nullable(Int64),\n `amount` Decimal(38, 6),\n `payment_date` Date,\n `last_update` Nullable(String)\n);\nCREATE TABLE rental (\n `rental_id` Int64,\n `rental_date` Date,\n `inventory_id` Int64,\n `customer_id` Int64,\n `return_date` Nullable(Date),\n `staff_id` Int64,\n `last_update` String\n);\nCREATE TABLE staff (\n `staff_id` Int64,\n `first_name` String,\n `last_name` String,\n `address_id` Int64,\n `picture` Nullable(String),\n `email` Nullable(String),\n `store_id` Int64,\n `active` String,\n `username` String,\n `password` Nullable(String),\n `last_update` String,\n `staff_description` Nullable(String)\n);\nCREATE TABLE store (\n `store_id` Int64,\n `manager_staff_id` Int64,\n `address_id` Int64,\n `last_update` String\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE actor (\n `actor_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `last_update` Nullable(String),\n `actor_description` Nullable(String),\n `actor_description_embedding` Array(Float32)\n);\nCREATE TABLE address (\n `address_id` Nullable(Int64),\n `address` Nullable(String),\n `address2` Nullable(String),\n `district` Nullable(String),\n `city_id` Nullable(Int64),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `last_update` Nullable(String),\n `address_description` Nullable(String),\n `address_description_embedding` Array(Float32)\n);\nCREATE TABLE category (\n `category_id` Nullable(Int64),\n `name` Nullable(String),\n `last_update` Nullable(String),\n `category_description` Nullable(String),\n `category_description_embedding` Array(Float32)\n);\nCREATE TABLE city (\n `city_id` Nullable(Int64),\n `city` Nullable(String),\n `country_id` Nullable(Int64),\n `last_update` Nullable(String),\n `city_description` Nullable(String),\n `city_description_embedding` Array(Float32)\n);\nCREATE TABLE country (\n `country_id` Nullable(Int64),\n `country` Nullable(String),\n `last_update` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE customer (\n `customer_id` Nullable(Int64),\n `store_id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email` Nullable(String),\n `address_id` Nullable(Int64),\n `active` Nullable(String),\n `create_date` Nullable(String),\n `last_update` Nullable(String),\n `customer_description` Nullable(String),\n `customer_description_embedding` Array(Float32)\n);\nCREATE TABLE film (\n `film_id` Nullable(Int64),\n `title` Nullable(String),\n `description` Nullable(String),\n `release_year` Nullable(String),\n `language_id` Nullable(Int64),\n `original_language_id` Nullable(Int64),\n `rental_duration` Nullable(Int64),\n `rental_rate` Nullable(Float64),\n `length` Nullable(Int64),\n `replacement_cost` Nullable(Float64),\n `rating` Nullable(String),\n `special_features` Nullable(String),\n `last_update` Nullable(String),\n `title_embedding` Array(Float32),\n `description_embedding` Array(Float32)\n);\nCREATE TABLE film_actor (\n `actor_id` Int64,\n `film_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_category (\n `film_id` Int64,\n `category_id` Int64,\n `last_update` String\n);\nCREATE TABLE film_text (\n `film_id` Int64,\n `title` String,\n `description` Nullable(String)\n);\nCREATE TABLE inventory (\n `inventory_id` Int64,\n `film_id` Int64,\n `store_id` Int64,\n `last_update` String\n);\nCREATE TABLE language (\n `language_id` Int64,\n `name` String,\n `last_update` String\n);\nCREATE TABLE payment (\n `payment_id` Int64,\n `customer_id` Int64,\n `staff_id` Int64,\n `rental_id` Nullable(Int64),\n `amount` Decimal(38, 6),\n `payment_date` Date,\n `last_update` Nullable(String)\n);\nCREATE TABLE rental (\n `rental_id` Int64,\n `rental_date` Date,\n `inventory_id` Int64,\n `customer_id` Int64,\n `return_date` Nullable(Date),\n `staff_id` Int64,\n `last_update` String\n);\nCREATE TABLE staff (\n `staff_id` Int64,\n `first_name` String,\n `last_name` String,\n `address_id` Int64,\n `picture` Nullable(String),\n `email` Nullable(String),\n `store_id` Int64,\n `active` String,\n `username` String,\n `password` Nullable(String),\n `last_update` String,\n `staff_description` Nullable(String)\n);\nCREATE TABLE store (\n `store_id` Int64,\n `manager_staff_id` Int64,\n `address_id` Int64,\n `last_update` String\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you provide the titles of the top 3 spectacular adventure films in which the actor with ID 5 has starred?\n\nLet's think step by step!\n" + }, + { + "db_id": "yelp", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An amazing dining experience with exquisite dishes.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT \n r.rid AS rid, \n r.business_id AS business_id, \n r.user_id AS user_id, \n r.text AS text, \n r.rating AS review_rating, \n distance(r.text_embedding, ref_vec_0) AS distance\n FROM \n review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n b.name AS name\nFROM\n CTE_Reviews cr\nJOIN\n business b ON toString(cr.business_id) = toString(b.business_id)\nWHERE\n b.city = 'San Francisco'\n AND b.rating > 4.0\nORDER BY\n cr.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "What are the names of the top five culinary havens in San Francisco, where diners have sung praises of mouth-watering delicacies, and whose glory shines with ratings above 4.0?", + "external_knowledge": "The vector search operation using `MATCH` performs an approximate nearest neighbor search, which tries to find the closest matches in a vector space based on a specified query embedding. Here, `lembed('all-MiniLM-L6-v2', \"An amazing dining experience with exquisite dishes.\")` generates an embedding for the phrase that is compared against the embeddings of review texts. The parameter `k = 5` specifies the retrieval of the top five reviews that are most similar to the vector query, sorted by Euclidean distance (L2 norm), where smaller distances indicate higher similarity. This technique allows semantic matching beyond simple text comparison, ideal for uncovering nuanced similarities in textual descriptions.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A delightful culinary journey with mouth-watering flavors.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Exceptional dining with highly praised dishes.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-rated restaurants with delicious meals.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly acclaimed eateries with outstanding cuisine.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Renowned dining spots with rave reviews.') AS ref_vec_0,\n\nCTE_Reviews AS (\n SELECT r.rid, r.business_id, r.user_id, r.text, r.rating AS review_rating, distance(r.text_embedding, ref_vec_0) AS distance FROM review r\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT b.name FROM CTE_Reviews cr JOIN business b ON toString(cr.business_id) = toString(b.business_id) WHERE b.city = 'San Francisco' AND b.rating > 4.0 ORDER BY cr.distance;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE business (\n `bid` Nullable(Int64),\n `business_id` Nullable(String),\n `name` Nullable(String),\n `full_address` Nullable(String),\n `city` Nullable(String),\n `latitude` Nullable(String),\n `longitude` Nullable(String),\n `review_count` Nullable(Int64),\n `is_open` Nullable(Int64),\n `rating` Nullable(Float64),\n `state` Nullable(String),\n `business_description` Nullable(String)\n);\nCREATE TABLE category (\n `id` Nullable(Int64),\n `business_id` Nullable(String),\n `category_name` Nullable(String),\n `category_description` Nullable(String)\n);\nCREATE TABLE checkin (\n `cid` Nullable(Int64),\n `business_id` Nullable(String),\n `count` Nullable(Int64),\n `day` Nullable(String)\n);\nCREATE TABLE neighbourhood (\n `id` Nullable(Int64),\n `business_id` Nullable(String),\n `neighbourhood_name` Nullable(String),\n `neighbourhood_description` Nullable(String)\n);\nCREATE TABLE review (\n `rid` Nullable(Int64),\n `business_id` Nullable(String),\n `user_id` Nullable(String),\n `rating` Nullable(Float64),\n `text` Nullable(String),\n `year` Nullable(Int64),\n `month` Nullable(String),\n `text_embedding` Array(Float32)\n);\nCREATE TABLE review_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE tip (\n `tip_id` Nullable(Int64),\n `business_id` Nullable(String),\n `text` Nullable(String),\n `user_id` Nullable(String),\n `likes` Nullable(Int64),\n `year` Nullable(Int64),\n `month` Nullable(String),\n `text_embedding` Array(Float32)\n);\nCREATE TABLE tip_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE user (\n `uid` Nullable(Int64),\n `user_id` Nullable(String),\n `name` Nullable(String),\n `user_description` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE business (\n `bid` Nullable(Int64),\n `business_id` Nullable(String),\n `name` Nullable(String),\n `full_address` Nullable(String),\n `city` Nullable(String),\n `latitude` Nullable(String),\n `longitude` Nullable(String),\n `review_count` Nullable(Int64),\n `is_open` Nullable(Int64),\n `rating` Nullable(Float64),\n `state` Nullable(String),\n `business_description` Nullable(String)\n);\nCREATE TABLE category (\n `id` Nullable(Int64),\n `business_id` Nullable(String),\n `category_name` Nullable(String),\n `category_description` Nullable(String)\n);\nCREATE TABLE checkin (\n `cid` Nullable(Int64),\n `business_id` Nullable(String),\n `count` Nullable(Int64),\n `day` Nullable(String)\n);\nCREATE TABLE neighbourhood (\n `id` Nullable(Int64),\n `business_id` Nullable(String),\n `neighbourhood_name` Nullable(String),\n `neighbourhood_description` Nullable(String)\n);\nCREATE TABLE review (\n `rid` Nullable(Int64),\n `business_id` Nullable(String),\n `user_id` Nullable(String),\n `rating` Nullable(Float64),\n `text` Nullable(String),\n `year` Nullable(Int64),\n `month` Nullable(String),\n `text_embedding` Array(Float32)\n);\nCREATE TABLE review_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE review_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE tip (\n `tip_id` Nullable(Int64),\n `business_id` Nullable(String),\n `text` Nullable(String),\n `user_id` Nullable(String),\n `likes` Nullable(Int64),\n `year` Nullable(Int64),\n `month` Nullable(String),\n `text_embedding` Array(Float32)\n);\nCREATE TABLE tip_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE tip_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE user (\n `uid` Nullable(Int64),\n `user_id` Nullable(String),\n `name` Nullable(String),\n `user_description` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe vector search operation using `MATCH` performs an approximate nearest neighbor search, which tries to find the closest matches in a vector space based on a specified query embedding. Here, `lembed('all-MiniLM-L6-v2', \"An amazing dining experience with exquisite dishes.\")` generates an embedding for the phrase that is compared against the embeddings of review texts. The parameter `k = 5` specifies the retrieval of the top five reviews that are most similar to the vector query, sorted by Euclidean distance (L2 norm), where smaller distances indicate higher similarity. This technique allows semantic matching beyond simple text comparison, ideal for uncovering nuanced similarities in textual descriptions.\nWhat are the names of the top five culinary havens in San Francisco, where diners have sung praises of mouth-watering delicacies, and whose glory shines with ratings above 4.0?\n\nLet's think step by step!\n" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Introduction to data science techniques and applications') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance\nFROM Courses\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the course that best aligns with the description \"Introduction to data science techniques and applications\" and provide its name.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Basics of data science methods and their applications') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Introductory course on data science principles and practices') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Fundamentals of data science and its practical uses') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Overview of data science approaches and real-world applications') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Introduction to data science concepts and their implementation') AS ref_vec_0\n\nSELECT course_name, distance(Courses.course_description_embedding, ref_vec_0) AS distance FROM Courses\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the course that best aligns with the description \"Introduction to data science techniques and applications\" and provide its name.\n\nLet's think step by step!\n" + }, + { + "db_id": "ship_mission", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A mission launched in the early 20th century capable of high speed') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance\nFROM mission\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Which mission launched in the early 20th century, known for high speed, closely fits that description?", + "external_knowledge": "- The `MATCH` operator is used in vector similarity searches to find items that are close in semantic space to a given input.\n- The `lembed()` function converts text into a vector representation using a specific machine learning model, in this case, `all-MiniLM-L6-v2`.\n- The parameter `k = 1` indicates that only one result, the most similar mission, is to be returned.\n- The similarity is measured using Euclidean distance (L2 norm), where a smaller distance signifies a higher similarity.\n- External knowledge: The phrase \"early 20th century\" refers to the period from 1900 to 1930, and \"high speed\" suggests missions with above-average velocity for that era.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A fast mission from the early 20th century') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-speed mission launched in the early 1900s') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Early 20th-century mission known for speed') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A mission from the early 1900s with high velocity') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Rapid mission initiated in the early 20th century') AS ref_vec_0\n\nSELECT Mission_ID, distance(mission.mission_description_embedding, ref_vec_0) AS distance FROM mission\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE mission (\n `Mission_ID` Nullable(Int64),\n `Ship_ID` Nullable(Int64),\n `Code` Nullable(String),\n `Launched_Year` Nullable(Int64),\n `Location` Nullable(String),\n `Speed_knots` Nullable(Int64),\n `Fate` Nullable(String),\n `mission_description` Nullable(String),\n `mission_description_embedding` Array(Float32)\n);\nCREATE TABLE mission_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE ship (\n `Ship_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Type` Nullable(String),\n `Nationality` Nullable(String),\n `Tonnage` Nullable(Int64),\n `ship_description` Nullable(String),\n `ship_description_embedding` Array(Float32)\n);\nCREATE TABLE ship_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE mission (\n `Mission_ID` Nullable(Int64),\n `Ship_ID` Nullable(Int64),\n `Code` Nullable(String),\n `Launched_Year` Nullable(Int64),\n `Location` Nullable(String),\n `Speed_knots` Nullable(Int64),\n `Fate` Nullable(String),\n `mission_description` Nullable(String),\n `mission_description_embedding` Array(Float32)\n);\nCREATE TABLE mission_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE mission_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE ship (\n `Ship_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Type` Nullable(String),\n `Nationality` Nullable(String),\n `Tonnage` Nullable(Int64),\n `ship_description` Nullable(String),\n `ship_description_embedding` Array(Float32)\n);\nCREATE TABLE ship_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE ship_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator is used in vector similarity searches to find items that are close in semantic space to a given input.\n- The `lembed()` function converts text into a vector representation using a specific machine learning model, in this case, `all-MiniLM-L6-v2`.\n- The parameter `k = 1` indicates that only one result, the most similar mission, is to be returned.\n- The similarity is measured using Euclidean distance (L2 norm), where a smaller distance signifies a higher similarity.\n- External knowledge: The phrase \"early 20th century\" refers to the period from 1900 to 1930, and \"high speed\" suggests missions with above-average velocity for that era.\nWhich mission launched in the early 20th century, known for high speed, closely fits that description?\n\nLet's think step by step!\n" + }, + { + "db_id": "flight_company", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Airport in Amsterdam, Netherlands') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Company incorporated in China') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description\nFROM flight f\nJOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id)\nJOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id)\nORDER BY f.id\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find a flight that involves a major airport in Amsterdam and is operated by a prominent company from China?", + "external_knowledge": "The vector search operations use the MATCH operator from the `sqlite-lembed` extension to perform approximate nearest neighbor (ANN) searches. The `lembed` function is applied to generate embeddings based on the provided descriptions. The parameter `k=5` specifies that the query retrieves the top 5 closest entities (for both airports and companies) as determined by their vector embedding similarity, typically using the Euclidean distance (L2 norm). In this context, a \"major airport\" or \"prominent company\" refers to those entities that are closest in description to the specified criteria, namely airports in Amsterdam and companies incorporated in China.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Major airport in Amsterdam') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Leading Chinese airline') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Amsterdam international airport') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Top airline from China') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Amsterdam Schiphol Airport') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Prominent Chinese aviation company') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Airport located in Amsterdam') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Chinese airline operator') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Hub airport in Amsterdam') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major airline from China') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(airport_description_embedding, ref_vec_0) AS distance\n FROM airport\n\n ORDER BY distance\n LIMIT 5\n),\n\noc_filtered AS (\n SELECT\n *,\n distance(operate_company_description_embedding, ref_vec_1) AS distance\n FROM operate_company\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT f.flight_description FROM flight f JOIN a_filtered AS a ON toString(f.airport_id) = toString(a.id) JOIN oc_filtered AS oc ON toString(f.company_id) = toString(oc.id) ORDER BY f.id LIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE airport (\n `id` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `IATA` Nullable(String),\n `ICAO` Nullable(String),\n `name` Nullable(String),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `id` Nullable(Int64),\n `Vehicle_Flight_number` Nullable(String),\n `Date` Nullable(String),\n `Pilot` Nullable(String),\n `Velocity` Nullable(Float64),\n `Altitude` Nullable(Float64),\n `airport_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);\nCREATE TABLE operate_company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Type` Nullable(String),\n `Principal_activities` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `operate_company_description` Nullable(String),\n `operate_company_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE airport (\n `id` Nullable(Int64),\n `City` Nullable(String),\n `Country` Nullable(String),\n `IATA` Nullable(String),\n `ICAO` Nullable(String),\n `name` Nullable(String),\n `airport_description` Nullable(String),\n `airport_description_embedding` Array(Float32)\n);\nCREATE TABLE flight (\n `id` Nullable(Int64),\n `Vehicle_Flight_number` Nullable(String),\n `Date` Nullable(String),\n `Pilot` Nullable(String),\n `Velocity` Nullable(Float64),\n `Altitude` Nullable(Float64),\n `airport_id` Nullable(Int64),\n `company_id` Nullable(Int64),\n `flight_description` Nullable(String),\n `flight_description_embedding` Array(Float32)\n);\nCREATE TABLE operate_company (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `Type` Nullable(String),\n `Principal_activities` Nullable(String),\n `Incorporated_in` Nullable(String),\n `Group_Equity_Shareholding` Nullable(Float64),\n `operate_company_description` Nullable(String),\n `operate_company_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe vector search operations use the MATCH operator from the `sqlite-lembed` extension to perform approximate nearest neighbor (ANN) searches. The `lembed` function is applied to generate embeddings based on the provided descriptions. The parameter `k=5` specifies that the query retrieves the top 5 closest entities (for both airports and companies) as determined by their vector embedding similarity, typically using the Euclidean distance (L2 norm). In this context, a \"major airport\" or \"prominent company\" refers to those entities that are closest in description to the specified criteria, namely airports in Amsterdam and companies incorporated in China.\nCan you find a flight that involves a major airport in Amsterdam and is operated by a prominent company from China?\n\nLet's think step by step!\n" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced machine learning techniques for big data analysis') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance\n FROM Student_Enrolment se\n JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country\nFROM Students s\nJOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id)\nJOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "sql_result_column_count": 6, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Where can I find the addresses of those students enrolled in some top programs focused on advanced machine learning for big data?", + "external_knowledge": "The `MATCH` operator performs an approximate nearest neighbor (ANN) search to find the most similar items based on vector embeddings. The embedding function `lembed('all-MiniLM-L6-v2', \"Advanced machine learning techniques for big data analysis\") creates a vector representation of the specified topic, which is then compared against the degree program summaries. The `k=5` condition restricts the results to the top 5 degree programs that have the highest similarity with this vector, using Euclidean distance as the measure of similarity. This approach allows for efficiently identifying degree programs that are most closely aligned with complex topics like machine learning in big data contexts.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-tier programs in machine learning for large-scale data') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced courses in machine learning and data analytics') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading programs for machine learning in big data environments') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Machine learning specialization for big data challenges') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);", + "WITH\n lembed('all-MiniLM-L6-v2', 'Elite programs in advanced machine learning for extensive data analysis') AS ref_vec_0,\n\nEnrolledStudents AS (\n SELECT se.student_id, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Student_Enrolment se JOIN Degree_Programs dp ON toString(se.degree_program_id) = toString(dp.degree_program_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.line_1, a.line_2, a.city, a.state_province_county, a.zip_postcode, a.country FROM Students s JOIN EnrolledStudents es ON toString(s.student_id) = toString(es.student_id) JOIN Addresses a ON toString(s.current_address_id) = toString(a.address_id);" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator performs an approximate nearest neighbor (ANN) search to find the most similar items based on vector embeddings. The embedding function `lembed('all-MiniLM-L6-v2', \"Advanced machine learning techniques for big data analysis\") creates a vector representation of the specified topic, which is then compared against the degree program summaries. The `k=5` condition restricts the results to the top 5 degree programs that have the highest similarity with this vector, using Euclidean distance as the measure of similarity. This approach allows for efficiently identifying degree programs that are most closely aligned with complex topics like machine learning in big data contexts.\nWhere can I find the addresses of those students enrolled in some top programs focused on advanced machine learning for big data?\n\nLet's think step by step!\n" + }, + { + "db_id": "car_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'luxury car') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId\n FROM continents c\n JOIN countries co ON toString(c.ContId) = toString(co.Continent)\n WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker\n FROM car_makers cm\n JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance\nFROM model_list ml\nJOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Top 3 luxury car models from European manufacturers.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'top luxury European cars') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'high-end European car models') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'premium European automobiles') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'luxury European vehicle models') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;", + "WITH\n lembed('all-MiniLM-L6-v2', 'exclusive European cars') AS ref_vec_0,\n\nContinentCountries AS (\n SELECT co.CountryId FROM continents c JOIN countries co ON toString(c.ContId) = toString(co.Continent) WHERE c.Continent = 'Europe'\n),\n\nFilteredCarMakers AS (\n SELECT cm.Id, cm.Maker FROM car_makers cm JOIN ContinentCountries cc ON toString(cm.Country) = toString(cc.CountryId)\n)\n\nSELECT ml.Model, distance(ml.model_list_description_embedding, ref_vec_0) AS distance FROM model_list ml JOIN FilteredCarMakers fcm ON toString(ml.Maker) = toString(fcm.Id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE car_makers (\n `Id` Nullable(Int64),\n `Maker` Nullable(String),\n `FullName` Nullable(String),\n `Country` Nullable(String),\n `car_makers_description` Nullable(String),\n `car_makers_description_embedding` Array(Float32)\n);\nCREATE TABLE car_names (\n `MakeId` Nullable(Int64),\n `Model` Nullable(String),\n `Make` Nullable(String),\n `car_names_description` Nullable(String),\n `car_names_description_embedding` Array(Float32)\n);\nCREATE TABLE cars_data (\n `Id` Nullable(Int64),\n `MPG` Nullable(String),\n `Cylinders` Nullable(Int64),\n `Edispl` Nullable(Float64),\n `Horsepower` Nullable(String),\n `Weight` Nullable(Int64),\n `Accelerate` Nullable(Float64),\n `Year` Nullable(Int64),\n `cars_data_description` Nullable(String),\n `cars_data_description_embedding` Array(Float32)\n);\nCREATE TABLE continents (\n `ContId` Nullable(Int64),\n `Continent` Nullable(String),\n `continents_description` Nullable(String)\n);\nCREATE TABLE countries (\n `CountryId` Nullable(Int64),\n `CountryName` Nullable(String),\n `Continent` Nullable(Int64),\n `countries_description` Nullable(String),\n `countries_description_embedding` Array(Float32)\n);\nCREATE TABLE model_list (\n `ModelId` Nullable(Int64),\n `Maker` Nullable(Int64),\n `Model` Nullable(String),\n `model_list_description` Nullable(String),\n `model_list_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE car_makers (\n `Id` Nullable(Int64),\n `Maker` Nullable(String),\n `FullName` Nullable(String),\n `Country` Nullable(String),\n `car_makers_description` Nullable(String),\n `car_makers_description_embedding` Array(Float32)\n);\nCREATE TABLE car_names (\n `MakeId` Nullable(Int64),\n `Model` Nullable(String),\n `Make` Nullable(String),\n `car_names_description` Nullable(String),\n `car_names_description_embedding` Array(Float32)\n);\nCREATE TABLE cars_data (\n `Id` Nullable(Int64),\n `MPG` Nullable(String),\n `Cylinders` Nullable(Int64),\n `Edispl` Nullable(Float64),\n `Horsepower` Nullable(String),\n `Weight` Nullable(Int64),\n `Accelerate` Nullable(Float64),\n `Year` Nullable(Int64),\n `cars_data_description` Nullable(String),\n `cars_data_description_embedding` Array(Float32)\n);\nCREATE TABLE continents (\n `ContId` Nullable(Int64),\n `Continent` Nullable(String),\n `continents_description` Nullable(String)\n);\nCREATE TABLE countries (\n `CountryId` Nullable(Int64),\n `CountryName` Nullable(String),\n `Continent` Nullable(Int64),\n `countries_description` Nullable(String),\n `countries_description_embedding` Array(Float32)\n);\nCREATE TABLE model_list (\n `ModelId` Nullable(Int64),\n `Maker` Nullable(Int64),\n `Model` Nullable(String),\n `model_list_description` Nullable(String),\n `model_list_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nTop 3 luxury car models from European manufacturers.\n\nLet's think step by step!\n" + }, + { + "db_id": "car_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A country known for its automotive industry and economic strength.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance\n FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description\n FROM car_makers cm\n JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId\nFROM CarMakerCountryJoin cmc\nORDER BY cmc.CarMakerId;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to find the IDs of car makers situated in the top 5 countries recognized for their automotive industry and economic strength. These countries should be identified based on a vector similarity search and the results must be sorted by car maker IDs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Countries leading in automotive manufacturing and economic prowess.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Nations excelling in car production and financial stability.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top countries for automotive industry and economic influence.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Leading nations in car industry and economic power.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Countries recognized for automotive sector and economic dominance.') AS ref_vec_0,\n\nCountryVectorSearch AS (\n SELECT CountryId, Continent, countries_description, distance(countries.countries_description_embedding, ref_vec_0) AS distance FROM countries\n ORDER BY distance\n LIMIT 5\n),\n\nCarMakerCountryJoin AS (\n SELECT cm.Id AS CarMakerId, cm.Maker, c.CountryId, c.countries_description FROM car_makers cm JOIN CountryVectorSearch c ON toString(cm.Country) = toString(c.CountryId)\n)\n\nSELECT cmc.CarMakerId FROM CarMakerCountryJoin cmc ORDER BY cmc.CarMakerId;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE car_makers (\n `Id` Nullable(Int64),\n `Maker` Nullable(String),\n `FullName` Nullable(String),\n `Country` Nullable(String),\n `car_makers_description` Nullable(String),\n `car_makers_description_embedding` Array(Float32)\n);\nCREATE TABLE car_names (\n `MakeId` Nullable(Int64),\n `Model` Nullable(String),\n `Make` Nullable(String),\n `car_names_description` Nullable(String),\n `car_names_description_embedding` Array(Float32)\n);\nCREATE TABLE cars_data (\n `Id` Nullable(Int64),\n `MPG` Nullable(String),\n `Cylinders` Nullable(Int64),\n `Edispl` Nullable(Float64),\n `Horsepower` Nullable(String),\n `Weight` Nullable(Int64),\n `Accelerate` Nullable(Float64),\n `Year` Nullable(Int64),\n `cars_data_description` Nullable(String),\n `cars_data_description_embedding` Array(Float32)\n);\nCREATE TABLE continents (\n `ContId` Nullable(Int64),\n `Continent` Nullable(String),\n `continents_description` Nullable(String)\n);\nCREATE TABLE countries (\n `CountryId` Nullable(Int64),\n `CountryName` Nullable(String),\n `Continent` Nullable(Int64),\n `countries_description` Nullable(String),\n `countries_description_embedding` Array(Float32)\n);\nCREATE TABLE model_list (\n `ModelId` Nullable(Int64),\n `Maker` Nullable(Int64),\n `Model` Nullable(String),\n `model_list_description` Nullable(String),\n `model_list_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE car_makers (\n `Id` Nullable(Int64),\n `Maker` Nullable(String),\n `FullName` Nullable(String),\n `Country` Nullable(String),\n `car_makers_description` Nullable(String),\n `car_makers_description_embedding` Array(Float32)\n);\nCREATE TABLE car_names (\n `MakeId` Nullable(Int64),\n `Model` Nullable(String),\n `Make` Nullable(String),\n `car_names_description` Nullable(String),\n `car_names_description_embedding` Array(Float32)\n);\nCREATE TABLE cars_data (\n `Id` Nullable(Int64),\n `MPG` Nullable(String),\n `Cylinders` Nullable(Int64),\n `Edispl` Nullable(Float64),\n `Horsepower` Nullable(String),\n `Weight` Nullable(Int64),\n `Accelerate` Nullable(Float64),\n `Year` Nullable(Int64),\n `cars_data_description` Nullable(String),\n `cars_data_description_embedding` Array(Float32)\n);\nCREATE TABLE continents (\n `ContId` Nullable(Int64),\n `Continent` Nullable(String),\n `continents_description` Nullable(String)\n);\nCREATE TABLE countries (\n `CountryId` Nullable(Int64),\n `CountryName` Nullable(String),\n `Continent` Nullable(Int64),\n `countries_description` Nullable(String),\n `countries_description_embedding` Array(Float32)\n);\nCREATE TABLE model_list (\n `ModelId` Nullable(Int64),\n `Maker` Nullable(Int64),\n `Model` Nullable(String),\n `model_list_description` Nullable(String),\n `model_list_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nI need to find the IDs of car makers situated in the top 5 countries recognized for their automotive industry and economic strength. These countries should be identified based on a vector similarity search and the results must be sorted by car maker IDs.\n\nLet's think step by step!\n" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advanced topics in technology and innovation') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance\nFROM Degree_Programs dp\nJOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "What are the names of the degree programs and their corresponding department names that most align with advanced topics in technology and innovation? Can you provide the top 5 matches?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Cutting-edge technology and innovation studies') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Programs focusing on technology advancements and innovation') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Technology and innovation-focused degree programs') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovation and advanced technology programs') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Degrees in technology innovation and advancements') AS ref_vec_0\n\nSELECT dp.degree_summary_name, d.department_name, distance(dp.degree_summary_description_embedding, ref_vec_0) AS distance FROM Degree_Programs dp JOIN Departments d ON toString(dp.department_id) = toString(d.department_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nWhat are the names of the degree programs and their corresponding department names that most align with advanced topics in technology and innovation? Can you provide the top 5 matches?\n\nLet's think step by step!\n" + }, + { + "db_id": "student_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'This teacher is excellent at interactive teaching methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance\n FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName\nFROM list l\nJOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom)\nORDER BY mt.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you find me the last names of the top 10 people from classrooms with the best 3 teachers who are really great at interactive teaching methods? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'This teacher excels in engaging students through interactive methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Outstanding teacher known for interactive teaching techniques.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Highly effective teacher using interactive teaching strategies.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Teacher skilled in interactive learning methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Interactive teaching expert with great methods.') AS ref_vec_0,\n\nMatchingTeachers AS (\n SELECT Classroom, distance(teachers.teachers_description_embedding, ref_vec_0) AS distance FROM teachers\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT l.LastName FROM list l JOIN MatchingTeachers mt ON toString(l.Classroom) = toString(mt.Classroom) ORDER BY mt.distance LIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE list (\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Grade` Nullable(Int64),\n `Classroom` Nullable(Int64),\n `list_description` Nullable(String),\n `list_description_embedding` Array(Float32)\n);\nCREATE TABLE teachers (\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Classroom` Nullable(Int64),\n `teachers_description` Nullable(String),\n `teachers_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE list (\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Grade` Nullable(Int64),\n `Classroom` Nullable(Int64),\n `list_description` Nullable(String),\n `list_description_embedding` Array(Float32)\n);\nCREATE TABLE teachers (\n `LastName` Nullable(String),\n `FirstName` Nullable(String),\n `Classroom` Nullable(Int64),\n `teachers_description` Nullable(String),\n `teachers_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you find me the last names of the top 10 people from classrooms with the best 3 teachers who are really great at interactive teaching methods? Thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "customer_complaints", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A new range of eco-friendly furniture') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance \nFROM Products\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "What is the name of the product that most likely fits the idea of brand-new eco-friendly furniture?", + "external_knowledge": "In vector searches using the SQLite extension \"sqlite-lembed,\" the MATCH operator facilitates an approximate nearest neighbor (ANN) search. This process finds the closest vectors to a given input by calculating Euclidean distances, where smaller distances indicate higher similarity. The `lembed('all-MiniLM-L6-v2', ...)` function converts text phrases into vector embeddings using a pre-trained model, enabling the database to perform semantic comparisons rather than exact matches. The `LIMIT 1` clause ensures that only the most relevant result is returned, focusing on the top match for the specified concept.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Innovative eco-friendly furniture collection') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Brand-new green furniture line') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Sustainable and modern furniture') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Environmentally conscious furniture designs') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Eco-friendly and stylish furniture options') AS ref_vec_0\n\nSELECT product_name, distance(Products.product_description_embedding, ref_vec_0) AS distance FROM Products\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Complaints (\n `complaint_id` Nullable(Int64),\n `product_id` Nullable(Int64),\n `customer_id` Nullable(Int64),\n `complaint_outcome_code` Nullable(String),\n `complaint_status_code` Nullable(String),\n `complaint_type_code` Nullable(String),\n `date_complaint_raised` Nullable(String),\n `date_complaint_closed` Nullable(String),\n `staff_id` Nullable(Int64),\n `Complaints_description` Nullable(String),\n `Complaints_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_type_code` Nullable(String),\n `address_line_1` Nullable(String),\n `address_line_2` Nullable(String),\n `town_city` Nullable(String),\n `state` Nullable(String),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `parent_product_id` Nullable(Int64),\n `product_category_code` Nullable(String),\n `date_product_first_available` Nullable(String),\n `date_product_discontinued` Nullable(String),\n `product_name` Nullable(String),\n `product_description` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Complaints (\n `complaint_id` Nullable(Int64),\n `product_id` Nullable(Int64),\n `customer_id` Nullable(Int64),\n `complaint_outcome_code` Nullable(String),\n `complaint_status_code` Nullable(String),\n `complaint_type_code` Nullable(String),\n `date_complaint_raised` Nullable(String),\n `date_complaint_closed` Nullable(String),\n `staff_id` Nullable(Int64),\n `Complaints_description` Nullable(String),\n `Complaints_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `customer_type_code` Nullable(String),\n `address_line_1` Nullable(String),\n `address_line_2` Nullable(String),\n `town_city` Nullable(String),\n `state` Nullable(String),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `parent_product_id` Nullable(Int64),\n `product_category_code` Nullable(String),\n `date_product_first_available` Nullable(String),\n `date_product_discontinued` Nullable(String),\n `product_name` Nullable(String),\n `product_description` Nullable(String),\n `product_price` Nullable(Float64),\n `product_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `gender` Nullable(String),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `email_address` Nullable(String),\n `phone_number` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIn vector searches using the SQLite extension \"sqlite-lembed,\" the MATCH operator facilitates an approximate nearest neighbor (ANN) search. This process finds the closest vectors to a given input by calculating Euclidean distances, where smaller distances indicate higher similarity. The `lembed('all-MiniLM-L6-v2', ...)` function converts text phrases into vector embeddings using a pre-trained model, enabling the database to perform semantic comparisons rather than exact matches. The `LIMIT 1` clause ensures that only the most relevant result is returned, focusing on the top match for the specified concept.\nWhat is the name of the product that most likely fits the idea of brand-new eco-friendly furniture?\n\nLet's think step by step!\n" + }, + { + "db_id": "department_store", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'High-quality jeans for summer') AS ref_vec_0,\n\nProductMatches AS (\n SELECT \n p.product_id AS product_id,\n p.product_name AS product_name,\n ps.supplier_id AS supplier_id,\n p.Products_description_embedding AS Products_description_embedding,\n distance(p.Products_description_embedding, ref_vec_0) AS distance\n FROM \n Products p\n JOIN \n Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT \n pm.product_name AS product_name,\n s.supplier_name AS supplier_name,\n pm.distance AS distance\n FROM \n ProductMatches pm\n JOIN\n Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT\n product_name,\n supplier_name\nFROM \n SupplierInfo\nORDER BY \n distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "**\n\nPlease provide the names of the top 5 suppliers who offer products most closely resembling high-quality jeans for summer, along with the names of these products.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Premium summer jeans') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Top-quality summer denim') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Best summer jeans') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'High-end summer denim') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Quality summer jeans') AS ref_vec_0,\n\nProductMatches AS (\n SELECT p.product_id, p.product_name, ps.supplier_id, p.Products_description_embedding, distance(p.Products_description_embedding, ref_vec_0) AS distance FROM Products p JOIN Product_Suppliers ps ON toString(p.product_id) = toString(ps.product_id)\n ORDER BY distance\n LIMIT 5\n),\n\nSupplierInfo AS (\n SELECT pm.product_name, s.supplier_name, pm.distance FROM ProductMatches pm JOIN Suppliers s ON toString(pm.supplier_id) = toString(s.supplier_id)\n)\n\nSELECT product_name, supplier_name FROM SupplierInfo ORDER BY distance LIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Addresses_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customer_Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status_code` String,\n `order_date` Date\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `customer_code` Nullable(String),\n `customer_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain (\n `dept_store_chain_id` Nullable(Int64),\n `dept_store_chain_name` Nullable(String),\n `Department_Store_Chain_description` Nullable(String),\n `Department_Store_Chain_description_embedding` Array(Float32)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Department_Stores (\n `dept_store_id` Nullable(Int64),\n `dept_store_chain_id` Nullable(Int64),\n `store_name` Nullable(String),\n `store_address` Nullable(String),\n `store_phone` Nullable(String),\n `store_email` Nullable(String),\n `Department_Stores_description` Nullable(String),\n `Department_Stores_description_embedding` Array(Float32)\n);\nCREATE TABLE Department_Stores_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `dept_store_id` Nullable(Int64),\n `department_name` Nullable(String),\n `Departments_description` Nullable(String),\n `Departments_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Nullable(Int64),\n `order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Product_Suppliers (\n `product_id` Int64,\n `supplier_id` Int64,\n `date_supplied_from` Date,\n `date_supplied_to` Nullable(Date),\n `total_amount_purchased` Nullable(String),\n `total_value_purchased` Nullable(Decimal(38, 6))\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_type_code` Nullable(String),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_gender` Nullable(String),\n `staff_name` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Department_Assignments (\n `staff_id` Int64,\n `department_id` Int64,\n `date_assigned_from` Date,\n `job_title_code` String,\n `date_assigned_to` Nullable(Date)\n);\nCREATE TABLE Staff_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Supplier_Addresses (\n `supplier_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Suppliers (\n `supplier_id` Nullable(Int64),\n `supplier_name` Nullable(String),\n `supplier_phone` Nullable(String),\n `Suppliers_description` Nullable(String),\n `Suppliers_description_embedding` Array(Float32)\n);\nCREATE TABLE Suppliers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `address_details` Nullable(String),\n `address_details_embedding` Array(Float32)\n);\nCREATE TABLE Addresses_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Addresses_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Customer_Addresses (\n `customer_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Customer_Orders (\n `order_id` Nullable(Int64),\n `customer_id` Int64,\n `order_status_code` String,\n `order_date` Date\n);\nCREATE TABLE Customers (\n `customer_id` Nullable(Int64),\n `payment_method_code` Nullable(String),\n `customer_code` Nullable(String),\n `customer_name` Nullable(String),\n `customer_address` Nullable(String),\n `customer_phone` Nullable(String),\n `customer_email` Nullable(String),\n `Customers_description` Nullable(String),\n `Customers_description_embedding` Array(Float32)\n);\nCREATE TABLE Customers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatachunks07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_metadatatext07 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Customers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain (\n `dept_store_chain_id` Nullable(Int64),\n `dept_store_chain_name` Nullable(String),\n `Department_Store_Chain_description` Nullable(String),\n `Department_Store_Chain_description_embedding` Array(Float32)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Store_Chain_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Department_Stores (\n `dept_store_id` Nullable(Int64),\n `dept_store_chain_id` Nullable(Int64),\n `store_name` Nullable(String),\n `store_address` Nullable(String),\n `store_phone` Nullable(String),\n `store_email` Nullable(String),\n `Department_Stores_description` Nullable(String),\n `Department_Stores_description_embedding` Array(Float32)\n);\nCREATE TABLE Department_Stores_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatachunks06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext05 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_metadatatext06 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Department_Stores_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `dept_store_id` Nullable(Int64),\n `department_name` Nullable(String),\n `Departments_description` Nullable(String),\n `Departments_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Departments_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Order_Items (\n `order_item_id` Nullable(Int64),\n `order_id` Int64,\n `product_id` Int64\n);\nCREATE TABLE Product_Suppliers (\n `product_id` Int64,\n `supplier_id` Int64,\n `date_supplied_from` Date,\n `date_supplied_to` Nullable(Date),\n `total_amount_purchased` Nullable(String),\n `total_value_purchased` Nullable(Decimal(38, 6))\n);\nCREATE TABLE Products (\n `product_id` Nullable(Int64),\n `product_type_code` Nullable(String),\n `product_name` Nullable(String),\n `product_price` Nullable(Float64),\n `Products_description` Nullable(String),\n `Products_description_embedding` Array(Float32)\n);\nCREATE TABLE Products_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatachunks04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_metadatatext04 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Products_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Staff (\n `staff_id` Nullable(Int64),\n `staff_gender` Nullable(String),\n `staff_name` Nullable(String),\n `Staff_description` Nullable(String),\n `Staff_description_embedding` Array(Float32)\n);\nCREATE TABLE Staff_Department_Assignments (\n `staff_id` Int64,\n `department_id` Int64,\n `date_assigned_from` Date,\n `job_title_code` String,\n `date_assigned_to` Nullable(Date)\n);\nCREATE TABLE Staff_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Staff_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\nCREATE TABLE Supplier_Addresses (\n `supplier_id` Int64,\n `address_id` Int64,\n `date_from` Date,\n `date_to` Nullable(Date)\n);\nCREATE TABLE Suppliers (\n `supplier_id` Nullable(Int64),\n `supplier_name` Nullable(String),\n `supplier_phone` Nullable(String),\n `Suppliers_description` Nullable(String),\n `Suppliers_description_embedding` Array(Float32)\n);\nCREATE TABLE Suppliers_metadatachunks00 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatachunks03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext01 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext02 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_metadatatext03 (\n `rowid` Nullable(String),\n `data` Nullable(String)\n);\nCREATE TABLE Suppliers_vector_chunks00 (\n `rowid` Nullable(String),\n `vectors` Nullable(String)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\n**\n\nPlease provide the names of the top 5 suppliers who offer products most closely resembling high-quality jeans for summer, along with the names of these products.\n\n**\n\nLet's think step by step!\n" + }, + { + "db_id": "student_transcripts_tracking", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'student interested in computer science and mathematics') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance\nFROM Students\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Reveal the identities and closeness of the top five intellectual explorers whose academic paths align with the realms of numbers and algorithms.", + "external_knowledge": "The \"MATCH\" operator in this query executes an approximate nearest neighbor (ANN) search, identifying items in the dataset that are most similar to a given vector based on specified criteria. The vector generated by the \"lembed\" function converts the text \"student interested in computer science and mathematics\" into a form that can be numerically compared to student embeddings. The \"k = 5\" clause specifies that the query should return the top five results, ordered by similarity. Similarity here is often measured using the Euclidean distance, where a smaller distance indicates a stronger alignment with the specified interests.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'student passionate about numerical analysis and algorithm development') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'academic enthusiast in the fields of computational theory and quantitative studies') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'learner focused on algorithmic structures and mathematical principles') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'explorer of mathematical models and algorithmic processes') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'individual dedicated to the study of algorithms and mathematics') AS ref_vec_0\n\nSELECT student_id, first_name, last_name, distance(Students.other_student_details_embedding, ref_vec_0) AS distance FROM Students\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Addresses (\n `address_id` Nullable(Int64),\n `line_1` Nullable(String),\n `line_2` Nullable(String),\n `line_3` Nullable(String),\n `city` Nullable(String),\n `zip_postcode` Nullable(String),\n `state_province_county` Nullable(String),\n `country` Nullable(String),\n `other_address_details` Nullable(String),\n `Addresses_description` Nullable(String),\n `other_address_details_embedding` Array(Float32)\n);\nCREATE TABLE Courses (\n `course_id` Nullable(Int64),\n `course_name` Nullable(String),\n `course_description` Nullable(String),\n `other_details` Nullable(String),\n `course_description_embedding` Array(Float32)\n);\nCREATE TABLE Degree_Programs (\n `degree_program_id` Nullable(Int64),\n `department_id` Nullable(Int64),\n `degree_summary_name` Nullable(String),\n `degree_summary_description` Nullable(String),\n `other_details` Nullable(String),\n `degree_summary_description_embedding` Array(Float32)\n);\nCREATE TABLE Departments (\n `department_id` Nullable(Int64),\n `department_name` Nullable(String),\n `department_description` Nullable(String),\n `other_details` Nullable(String),\n `department_description_embedding` Array(Float32)\n);\nCREATE TABLE Sections (\n `section_id` Nullable(Int64),\n `course_id` Nullable(Int64),\n `section_name` Nullable(String),\n `section_description` Nullable(String),\n `other_details` Nullable(String),\n `section_description_embedding` Array(Float32)\n);\nCREATE TABLE Semesters (\n `semester_id` Nullable(Int64),\n `semester_name` Nullable(String),\n `semester_description` Nullable(String),\n `other_details` Nullable(String),\n `semester_description_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment (\n `student_enrolment_id` Nullable(Int64),\n `degree_program_id` Nullable(Int64),\n `semester_id` Nullable(Int64),\n `student_id` Nullable(Int64),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\nCREATE TABLE Student_Enrolment_Courses (\n `student_course_id` Nullable(Int64),\n `course_id` Int64,\n `student_enrolment_id` Int64\n);\nCREATE TABLE Students (\n `student_id` Nullable(Int64),\n `current_address_id` Nullable(Int64),\n `permanent_address_id` Nullable(Int64),\n `first_name` Nullable(String),\n `middle_name` Nullable(String),\n `last_name` Nullable(String),\n `cell_mobile_number` Nullable(String),\n `email_address` Nullable(String),\n `ssn` Nullable(String),\n `date_first_registered` Nullable(String),\n `date_left` Nullable(String),\n `other_student_details` Nullable(String),\n `Students_description` Nullable(String),\n `other_student_details_embedding` Array(Float32)\n);\nCREATE TABLE Transcript_Contents (\n `student_course_id` Int64,\n `transcript_id` Int64\n);\nCREATE TABLE Transcripts (\n `transcript_id` Nullable(Int64),\n `transcript_date` Nullable(String),\n `other_details` Nullable(String),\n `other_details_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe \"MATCH\" operator in this query executes an approximate nearest neighbor (ANN) search, identifying items in the dataset that are most similar to a given vector based on specified criteria. The vector generated by the \"lembed\" function converts the text \"student interested in computer science and mathematics\" into a form that can be numerically compared to student embeddings. The \"k = 5\" clause specifies that the query should return the top five results, ordered by similarity. Similarity here is often measured using the Euclidean distance, where a smaller distance indicates a stronger alignment with the specified interests.\nReveal the identities and closeness of the top five intellectual explorers whose academic paths align with the realms of numbers and algorithms.\n\nLet's think step by step!\n" + }, + { + "db_id": "store_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A famous heavy metal band from the 1980s known for their international success') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance \nFROM artists\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the artist who is most representative of a famous heavy metal band from the 1980s known for their international success, and provide their ID along with the similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary heavy metal band from the 1980s with global acclaim') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic 1980s heavy metal band famous worldwide') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned heavy metal group from the 1980s with international fame') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A prominent 1980s heavy metal band celebrated globally') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known heavy metal band from the 1980s with worldwide success') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE albums (\n `id` Nullable(Int64),\n `title` Nullable(String),\n `artist_id` Nullable(Int64),\n `albums_description` Nullable(String),\n `albums_description_embedding` Array(Float32)\n);\nCREATE TABLE artists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `artists_description` Nullable(String),\n `artists_description_embedding` Array(Float32)\n);\nCREATE TABLE customers (\n `id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `company` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `support_rep_id` Nullable(Int64),\n `customers_description` Nullable(String),\n `customers_description_embedding` Array(Float32)\n);\nCREATE TABLE employees (\n `id` Nullable(Int64),\n `last_name` String,\n `first_name` String,\n `title` Nullable(String),\n `reports_to` Nullable(Int64),\n `birth_date` Nullable(String),\n `hire_date` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `employees_description` Nullable(String)\n);\nCREATE TABLE genres (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `genres_description` Nullable(String),\n `genres_description_embedding` Array(Float32)\n);\nCREATE TABLE invoice_lines (\n `id` Nullable(Int64),\n `invoice_id` Int64,\n `track_id` Int64,\n `unit_price` Decimal(38, 6),\n `quantity` Int64\n);\nCREATE TABLE invoices (\n `id` Nullable(Int64),\n `customer_id` Int64,\n `invoice_date` String,\n `billing_address` Nullable(String),\n `billing_city` Nullable(String),\n `billing_state` Nullable(String),\n `billing_country` Nullable(String),\n `billing_postal_code` Nullable(String),\n `total` Decimal(38, 6),\n `invoices_description` Nullable(String)\n);\nCREATE TABLE media_types (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `media_types_description` Nullable(String),\n `media_types_description_embedding` Array(Float32)\n);\nCREATE TABLE playlist_tracks (\n `playlist_id` Int64,\n `track_id` Int64\n);\nCREATE TABLE playlists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `playlists_description` Nullable(String),\n `playlists_description_embedding` Array(Float32)\n);\nCREATE TABLE tracks (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `album_id` Nullable(Int64),\n `media_type_id` Nullable(Int64),\n `genre_id` Nullable(Int64),\n `composer` Nullable(String),\n `milliseconds` Nullable(Int64),\n `bytes` Nullable(Int64),\n `unit_price` Nullable(Float64),\n `tracks_description` Nullable(String),\n `tracks_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE albums (\n `id` Nullable(Int64),\n `title` Nullable(String),\n `artist_id` Nullable(Int64),\n `albums_description` Nullable(String),\n `albums_description_embedding` Array(Float32)\n);\nCREATE TABLE artists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `artists_description` Nullable(String),\n `artists_description_embedding` Array(Float32)\n);\nCREATE TABLE customers (\n `id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `company` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `support_rep_id` Nullable(Int64),\n `customers_description` Nullable(String),\n `customers_description_embedding` Array(Float32)\n);\nCREATE TABLE employees (\n `id` Nullable(Int64),\n `last_name` String,\n `first_name` String,\n `title` Nullable(String),\n `reports_to` Nullable(Int64),\n `birth_date` Nullable(String),\n `hire_date` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `employees_description` Nullable(String)\n);\nCREATE TABLE genres (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `genres_description` Nullable(String),\n `genres_description_embedding` Array(Float32)\n);\nCREATE TABLE invoice_lines (\n `id` Nullable(Int64),\n `invoice_id` Int64,\n `track_id` Int64,\n `unit_price` Decimal(38, 6),\n `quantity` Int64\n);\nCREATE TABLE invoices (\n `id` Nullable(Int64),\n `customer_id` Int64,\n `invoice_date` String,\n `billing_address` Nullable(String),\n `billing_city` Nullable(String),\n `billing_state` Nullable(String),\n `billing_country` Nullable(String),\n `billing_postal_code` Nullable(String),\n `total` Decimal(38, 6),\n `invoices_description` Nullable(String)\n);\nCREATE TABLE media_types (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `media_types_description` Nullable(String),\n `media_types_description_embedding` Array(Float32)\n);\nCREATE TABLE playlist_tracks (\n `playlist_id` Int64,\n `track_id` Int64\n);\nCREATE TABLE playlists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `playlists_description` Nullable(String),\n `playlists_description_embedding` Array(Float32)\n);\nCREATE TABLE tracks (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `album_id` Nullable(Int64),\n `media_type_id` Nullable(Int64),\n `genre_id` Nullable(Int64),\n `composer` Nullable(String),\n `milliseconds` Nullable(Int64),\n `bytes` Nullable(Int64),\n `unit_price` Nullable(Float64),\n `tracks_description` Nullable(String),\n `tracks_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nIdentify the artist who is most representative of a famous heavy metal band from the 1980s known for their international success, and provide their ID along with the similarity distance.\n\nLet's think step by step!\n" + }, + { + "db_id": "election", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party fielded a strong lineup for the major state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance \nFROM party\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Could you identify which political party is most closely associated with having a strong lineup for major state positions in 2020, as per the description provided? Please return only the top match.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party was noted for its strong candidates in key state roles.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Republican party in 2020 had a formidable lineup for major state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party was recognized for having a strong presence in significant state roles.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'The Republican party in 2020 assembled a strong roster for important state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'In 2020, the Republican party was associated with a robust lineup for key state positions.') AS ref_vec_0\n\nSELECT Party, distance(party.party_description_embedding, ref_vec_0) AS distance FROM party\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE county (\n `County_Id` Nullable(Int64),\n `County_name` Nullable(String),\n `Population` Nullable(Float64),\n `Zip_code` Nullable(String),\n `county_description` Nullable(String),\n `county_description_embedding` Array(Float32)\n);\nCREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Counties_Represented` Nullable(String),\n `District` Nullable(Int64),\n `Delegate` Nullable(String),\n `Party` Nullable(Int64),\n `First_Elected` Nullable(Float64),\n `Committee` Nullable(String),\n `election_description` Nullable(String),\n `election_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Party` Nullable(String),\n `Governor` Nullable(String),\n `Lieutenant_Governor` Nullable(String),\n `Comptroller` Nullable(String),\n `Attorney_General` Nullable(String),\n `US_Senate` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE county (\n `County_Id` Nullable(Int64),\n `County_name` Nullable(String),\n `Population` Nullable(Float64),\n `Zip_code` Nullable(String),\n `county_description` Nullable(String),\n `county_description_embedding` Array(Float32)\n);\nCREATE TABLE election (\n `Election_ID` Nullable(Int64),\n `Counties_Represented` Nullable(String),\n `District` Nullable(Int64),\n `Delegate` Nullable(String),\n `Party` Nullable(Int64),\n `First_Elected` Nullable(Float64),\n `Committee` Nullable(String),\n `election_description` Nullable(String),\n `election_description_embedding` Array(Float32)\n);\nCREATE TABLE party (\n `Party_ID` Nullable(Int64),\n `Year` Nullable(Float64),\n `Party` Nullable(String),\n `Governor` Nullable(String),\n `Lieutenant_Governor` Nullable(String),\n `Comptroller` Nullable(String),\n `Attorney_General` Nullable(String),\n `US_Senate` Nullable(String),\n `party_description` Nullable(String),\n `party_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you identify which political party is most closely associated with having a strong lineup for major state positions in 2020, as per the description provided? Please return only the top match.\n\nLet's think step by step!\n" + }, + { + "db_id": "coffee_shop", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A coffee shop located at 123 Elm Street with 10 staff members, a score of 45.5, and opened in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance \nFROM shop\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me the Shop_ID and its similarity score for the coffee shop that most closely matches the description of being located at 123 Elm Street, having 10 staff members, a score of 45.5, and opened in 2015?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Coffee shop at 123 Elm Street, staffed by 10 people, rated 45.5, opened in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Located at 123 Elm Street, this coffee shop employs 10 staff, has a score of 45.5, and started operations in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A coffee shop near 123 Elm Street, with 10 employees, a rating of 45.5, and established in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Coffee shop situated at 123 Elm Street, with 10 staff members, a score of 45.5, and opened in the year 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Shop at 123 Elm Street, featuring 10 staff, a score of 45.5, and opened in 2015.') AS ref_vec_0\n\nSELECT Shop_ID, distance(shop.shop_description_embedding, ref_vec_0) AS distance FROM shop\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE happy_hour (\n `HH_ID` Nullable(Int64),\n `Shop_ID` Nullable(Int64),\n `Month` Nullable(String),\n `Num_of_shaff_in_charge` Nullable(Int64)\n);\nCREATE TABLE happy_hour_member (\n `HH_ID` Nullable(Int64),\n `Member_ID` Nullable(Int64),\n `Total_amount` Nullable(Float64)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Membership_card` Nullable(String),\n `Age` Nullable(Int64),\n `Time_of_purchase` Nullable(Int64),\n `Level_of_membership` Nullable(Int64),\n `Address` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Address` Nullable(String),\n `Num_of_staff` Nullable(String),\n `Score` Nullable(Float64),\n `Open_Year` Nullable(String),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE happy_hour (\n `HH_ID` Nullable(Int64),\n `Shop_ID` Nullable(Int64),\n `Month` Nullable(String),\n `Num_of_shaff_in_charge` Nullable(Int64)\n);\nCREATE TABLE happy_hour_member (\n `HH_ID` Nullable(Int64),\n `Member_ID` Nullable(Int64),\n `Total_amount` Nullable(Float64)\n);\nCREATE TABLE member (\n `Member_ID` Nullable(Int64),\n `Name` Nullable(String),\n `Membership_card` Nullable(String),\n `Age` Nullable(Int64),\n `Time_of_purchase` Nullable(Int64),\n `Level_of_membership` Nullable(Int64),\n `Address` Nullable(String),\n `member_description` Nullable(String),\n `member_description_embedding` Array(Float32)\n);\nCREATE TABLE shop (\n `Shop_ID` Nullable(Int64),\n `Address` Nullable(String),\n `Num_of_staff` Nullable(String),\n `Score` Nullable(Float64),\n `Open_Year` Nullable(String),\n `shop_description` Nullable(String),\n `shop_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the Shop_ID and its similarity score for the coffee shop that most closely matches the description of being located at 123 Elm Street, having 10 staff members, a score of 45.5, and opened in 2015?\n\nLet's think step by step!\n" + }, + { + "db_id": "match_season", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A player with a short career in 2011, having no match outcomes recorded.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance\nFROM player\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you find five players who had brief careers around 2011 and didn't have recorded match outcomes?", + "external_knowledge": "The `MATCH` operator along with the `lembed` function performs a vector similarity search, known as approximate nearest neighbor (ANN) search. This operation identifies items resembling a specified vector phrase, ranking them by similarity. The `k = 5` parameter indicates that the search returns the five closest matches. Similarity is assessed using Euclidean distance (L2 norm), where lower distance values reflect higher similarity. The `lembed` model, `all-MiniLM-L6-v2`, is used to generate meaningful embeddings of text descriptions to facilitate this comparison.", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'Players with brief stints in 2011 and no match results.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Individuals with short playing periods in 2011, lacking match records.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Athletes who played only briefly in 2011 without any match outcomes.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Players with limited careers during 2011 and missing match data.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;", + "WITH\n lembed('all-MiniLM-L6-v2', 'Competitors with short 2011 careers without recorded match results.') AS ref_vec_0\n\nSELECT Player_ID, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 4, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_id` Nullable(Int64),\n `Country_name` Nullable(String),\n `Capital` Nullable(String),\n `Official_native_language` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE match_season (\n `Season` Nullable(Float64),\n `Player` Nullable(String),\n `Position` Nullable(String),\n `Country` Nullable(Int64),\n `Team` Nullable(Int64),\n `Draft_Pick_Number` Nullable(Int64),\n `Draft_Class` Nullable(String),\n `College` Nullable(String),\n `match_season_description` Nullable(String),\n `match_season_description_embedding` Array(Float32)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Years_Played` Nullable(String),\n `Total_WL` Nullable(String),\n `Singles_WL` Nullable(String),\n `Doubles_WL` Nullable(String),\n `Team` Nullable(Int64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE team (\n `Team_id` Nullable(Int64),\n `Name` Nullable(String),\n `team_description` Nullable(String),\n `team_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE country (\n `Country_id` Nullable(Int64),\n `Country_name` Nullable(String),\n `Capital` Nullable(String),\n `Official_native_language` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE match_season (\n `Season` Nullable(Float64),\n `Player` Nullable(String),\n `Position` Nullable(String),\n `Country` Nullable(Int64),\n `Team` Nullable(Int64),\n `Draft_Pick_Number` Nullable(Int64),\n `Draft_Class` Nullable(String),\n `College` Nullable(String),\n `match_season_description` Nullable(String),\n `match_season_description_embedding` Array(Float32)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Years_Played` Nullable(String),\n `Total_WL` Nullable(String),\n `Singles_WL` Nullable(String),\n `Doubles_WL` Nullable(String),\n `Team` Nullable(Int64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE team (\n `Team_id` Nullable(Int64),\n `Name` Nullable(String),\n `team_description` Nullable(String),\n `team_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator along with the `lembed` function performs a vector similarity search, known as approximate nearest neighbor (ANN) search. This operation identifies items resembling a specified vector phrase, ranking them by similarity. The `k = 5` parameter indicates that the search returns the five closest matches. Similarity is assessed using Euclidean distance (L2 norm), where lower distance values reflect higher similarity. The `lembed` model, `all-MiniLM-L6-v2`, is used to generate meaningful embeddings of text descriptions to facilitate this comparison.\nCould you find five players who had brief careers around 2011 and didn't have recorded match outcomes?\n\nLet's think step by step!\n" + }, + { + "db_id": "match_season", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An outstanding player with a remarkable performance record in singles matches.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance\nFROM player\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the player who is considered an outstanding performer in singles matches based on their playing record?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'A player with exceptional singles match achievements.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A singles match performer with outstanding records.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A top performer known for singles success.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'An athlete with impressive singles match history.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A player distinguished by excellent singles performance.') AS ref_vec_0\n\nSELECT Player, distance(player.player_description_embedding, ref_vec_0) AS distance FROM player\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE country (\n `Country_id` Nullable(Int64),\n `Country_name` Nullable(String),\n `Capital` Nullable(String),\n `Official_native_language` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE match_season (\n `Season` Nullable(Float64),\n `Player` Nullable(String),\n `Position` Nullable(String),\n `Country` Nullable(Int64),\n `Team` Nullable(Int64),\n `Draft_Pick_Number` Nullable(Int64),\n `Draft_Class` Nullable(String),\n `College` Nullable(String),\n `match_season_description` Nullable(String),\n `match_season_description_embedding` Array(Float32)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Years_Played` Nullable(String),\n `Total_WL` Nullable(String),\n `Singles_WL` Nullable(String),\n `Doubles_WL` Nullable(String),\n `Team` Nullable(Int64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE team (\n `Team_id` Nullable(Int64),\n `Name` Nullable(String),\n `team_description` Nullable(String),\n `team_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE country (\n `Country_id` Nullable(Int64),\n `Country_name` Nullable(String),\n `Capital` Nullable(String),\n `Official_native_language` Nullable(String),\n `country_description` Nullable(String),\n `country_description_embedding` Array(Float32)\n);\nCREATE TABLE match_season (\n `Season` Nullable(Float64),\n `Player` Nullable(String),\n `Position` Nullable(String),\n `Country` Nullable(Int64),\n `Team` Nullable(Int64),\n `Draft_Pick_Number` Nullable(Int64),\n `Draft_Class` Nullable(String),\n `College` Nullable(String),\n `match_season_description` Nullable(String),\n `match_season_description_embedding` Array(Float32)\n);\nCREATE TABLE player (\n `Player_ID` Nullable(Int64),\n `Player` Nullable(String),\n `Years_Played` Nullable(String),\n `Total_WL` Nullable(String),\n `Singles_WL` Nullable(String),\n `Doubles_WL` Nullable(String),\n `Team` Nullable(Int64),\n `player_description` Nullable(String),\n `player_description_embedding` Array(Float32)\n);\nCREATE TABLE team (\n `Team_id` Nullable(Int64),\n `Name` Nullable(String),\n `team_description` Nullable(String),\n `team_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the player who is considered an outstanding performer in singles matches based on their playing record?\n\nLet's think step by step!\n" + }, + { + "db_id": "store_1", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary rock band with timeless hits and energetic performances.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance\nFROM artists\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you help me find the id of an artist who's like a legendary rock band with timeless hits and energetic performances?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic rock band known for its ageless songs and dynamic stage presence.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A renowned rock group with classic tracks and high-energy shows.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A famous rock band celebrated for its enduring music and lively performances.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A legendary rock ensemble with unforgettable hits and vibrant live acts.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;", + "WITH\n lembed('all-MiniLM-L6-v2', 'A well-known rock band with timeless songs and electrifying concerts.') AS ref_vec_0\n\nSELECT id, distance(artists.artists_description_embedding, ref_vec_0) AS distance FROM artists\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE albums (\n `id` Nullable(Int64),\n `title` Nullable(String),\n `artist_id` Nullable(Int64),\n `albums_description` Nullable(String),\n `albums_description_embedding` Array(Float32)\n);\nCREATE TABLE artists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `artists_description` Nullable(String),\n `artists_description_embedding` Array(Float32)\n);\nCREATE TABLE customers (\n `id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `company` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `support_rep_id` Nullable(Int64),\n `customers_description` Nullable(String),\n `customers_description_embedding` Array(Float32)\n);\nCREATE TABLE employees (\n `id` Nullable(Int64),\n `last_name` String,\n `first_name` String,\n `title` Nullable(String),\n `reports_to` Nullable(Int64),\n `birth_date` Nullable(String),\n `hire_date` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `employees_description` Nullable(String)\n);\nCREATE TABLE genres (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `genres_description` Nullable(String),\n `genres_description_embedding` Array(Float32)\n);\nCREATE TABLE invoice_lines (\n `id` Nullable(Int64),\n `invoice_id` Int64,\n `track_id` Int64,\n `unit_price` Decimal(38, 6),\n `quantity` Int64\n);\nCREATE TABLE invoices (\n `id` Nullable(Int64),\n `customer_id` Int64,\n `invoice_date` String,\n `billing_address` Nullable(String),\n `billing_city` Nullable(String),\n `billing_state` Nullable(String),\n `billing_country` Nullable(String),\n `billing_postal_code` Nullable(String),\n `total` Decimal(38, 6),\n `invoices_description` Nullable(String)\n);\nCREATE TABLE media_types (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `media_types_description` Nullable(String),\n `media_types_description_embedding` Array(Float32)\n);\nCREATE TABLE playlist_tracks (\n `playlist_id` Int64,\n `track_id` Int64\n);\nCREATE TABLE playlists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `playlists_description` Nullable(String),\n `playlists_description_embedding` Array(Float32)\n);\nCREATE TABLE tracks (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `album_id` Nullable(Int64),\n `media_type_id` Nullable(Int64),\n `genre_id` Nullable(Int64),\n `composer` Nullable(String),\n `milliseconds` Nullable(Int64),\n `bytes` Nullable(Int64),\n `unit_price` Nullable(Float64),\n `tracks_description` Nullable(String),\n `tracks_description_embedding` Array(Float32)\n);", + "embedding_model_name": "all-MiniLM-L6-v2", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE albums (\n `id` Nullable(Int64),\n `title` Nullable(String),\n `artist_id` Nullable(Int64),\n `albums_description` Nullable(String),\n `albums_description_embedding` Array(Float32)\n);\nCREATE TABLE artists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `artists_description` Nullable(String),\n `artists_description_embedding` Array(Float32)\n);\nCREATE TABLE customers (\n `id` Nullable(Int64),\n `first_name` Nullable(String),\n `last_name` Nullable(String),\n `company` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `support_rep_id` Nullable(Int64),\n `customers_description` Nullable(String),\n `customers_description_embedding` Array(Float32)\n);\nCREATE TABLE employees (\n `id` Nullable(Int64),\n `last_name` String,\n `first_name` String,\n `title` Nullable(String),\n `reports_to` Nullable(Int64),\n `birth_date` Nullable(String),\n `hire_date` Nullable(String),\n `address` Nullable(String),\n `city` Nullable(String),\n `state` Nullable(String),\n `country` Nullable(String),\n `postal_code` Nullable(String),\n `phone` Nullable(String),\n `fax` Nullable(String),\n `email` Nullable(String),\n `employees_description` Nullable(String)\n);\nCREATE TABLE genres (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `genres_description` Nullable(String),\n `genres_description_embedding` Array(Float32)\n);\nCREATE TABLE invoice_lines (\n `id` Nullable(Int64),\n `invoice_id` Int64,\n `track_id` Int64,\n `unit_price` Decimal(38, 6),\n `quantity` Int64\n);\nCREATE TABLE invoices (\n `id` Nullable(Int64),\n `customer_id` Int64,\n `invoice_date` String,\n `billing_address` Nullable(String),\n `billing_city` Nullable(String),\n `billing_state` Nullable(String),\n `billing_country` Nullable(String),\n `billing_postal_code` Nullable(String),\n `total` Decimal(38, 6),\n `invoices_description` Nullable(String)\n);\nCREATE TABLE media_types (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `media_types_description` Nullable(String),\n `media_types_description_embedding` Array(Float32)\n);\nCREATE TABLE playlist_tracks (\n `playlist_id` Int64,\n `track_id` Int64\n);\nCREATE TABLE playlists (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `playlists_description` Nullable(String),\n `playlists_description_embedding` Array(Float32)\n);\nCREATE TABLE tracks (\n `id` Nullable(Int64),\n `name` Nullable(String),\n `album_id` Nullable(Int64),\n `media_type_id` Nullable(Int64),\n `genre_id` Nullable(Int64),\n `composer` Nullable(String),\n `milliseconds` Nullable(Int64),\n `bytes` Nullable(Int64),\n `unit_price` Nullable(Float64),\n `tracks_description` Nullable(String),\n `tracks_description_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'all-MiniLM-L6-v2'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nall-MiniLM-L6-v2\n\n## NATURAL LANGUAGE QUESTION\nHey, can you help me find the id of an artist who's like a legendary rock band with timeless hits and energetic performances?\n\nLet's think step by step!\n" + } +] \ No newline at end of file diff --git a/benchmark/data/results/test/olympics/olympics_qs.json b/benchmark/data/results/test/olympics/olympics_qs.json new file mode 100644 index 0000000..0a76e78 --- /dev/null +++ b/benchmark/data/results/test/olympics/olympics_qs.json @@ -0,0 +1,603 @@ +[ + { + "question": "Could you show me the 5 cities that are most representative of a capital city with a rich history and vibrant culture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'a rich history and vibrant culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you show me the IDs and names of the 3 games most related to the Olympic games held in summer?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic games held in summer') AS ref_vec_0\n\nSELECT id, games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Identify the top 5 cities characterized as bustling with rich history and vibrant culture, and provide their IDs, names, descriptions, and similarity distances.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'bustling with rich history and vibrant culture') AS ref_vec_0\n\nSELECT city_name, city_description, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Can you locate that city known for being a lively metropolitan hub with a deep historical backdrop and contemporary attractions?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'a lively metropolitan hub with a deep historical backdrop and contemporary attractions') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "**User**: \"I'm interested in learning about different cities.\"\n**Assistant**: \"Could you describe the type of city you're looking for?\"\n**User**: \"I'm looking for a city that's vibrant and known for its rich culture and history.\"\n**Assistant**: \"Got it. How many cities would you like information on?\"\n**User**: \"Just one city for now.\"\n**Assistant**: \"Alright, I will identify one city that fits your description the best.\"\n**User**: \"Perfect, what details will you provide about this city?\"\n**Assistant**: \"I will provide the ID and name of the city for you.\"\n**User**: \"Sounds good, thank you!\"\n**Assistant**: \"You're welcome. I'll proceed with retrieving that information for you.\"", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'vibrant and known for its rich culture and history') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey! Could you snag the names and distances of the top 5 cities that are like a bustling metropolis with lots of cultural landmarks? Thanks!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'a bustling metropolis with lots of cultural landmarks') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Hey, can you find the top 5 cities that are all about vibrant culture and lively markets? I need their IDs and how closely they match the vibe.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'vibrant culture and lively markets') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "** \nIdentify and return the ID, name, description, embedding, and similarity distance of the top 5 cities that are renowned for their rich cultural heritage and vibrant nightlife. \n**", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'are renowned for their rich cultural heritage and vibrant nightlife') AS ref_vec_0\n\nSELECT city_name, city_description, city_description_embedding, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Identify the IDs of the three cities that align most closely with the description of being bustling urban areas known for their cultural heritage and vibrant nightlife.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'being bustling urban areas known for their cultural heritage and vibrant nightlife') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Can you tell me the name of a bustling city that's famous for its lively culture and historical sites?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'a bustling city that's famous for its lively culture and historical sites?') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Could you show me the top 5 cities that hosted games most associated with winter conditions in a snowy region, and list their names and IDs?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'most associated with winter conditions in a snowy region') AS ref_vec_0,\n\nRankedGames AS (\n SELECT \n g.id AS games_id, \n g.games_name AS games_name, \n g.games_year AS games_year, \n g.season AS season, \n distance(g.games_description_embedding, ref_vec_0) AS games_distance\n FROM games AS g\n ORDER BY games_distance\n LIMIT 5\n),\n\nCityGames AS (\n SELECT \n c.id AS city_id, \n c.city_name AS city_name, \n cg.games_id AS games_id\n FROM city AS c\n JOIN games_city AS cg ON toString(c.id) = toString(cg.city_id)\n WHERE cg.games_id IN (SELECT games_id FROM RankedGames)\n)\n\nSELECT \n c.city_id AS city_id, \n c.city_name AS city_name\nFROM CityGames AS c\nJOIN RankedGames AS rg ON toString(c.games_id) = toString(rg.games_id)\nORDER BY rg.games_distance\nLIMIT 5;" + }, + { + "question": "What are the top 3 cities known for their cultural richness and vibrant arts, based on their description similarity? Please list their IDs, names, and similarity distances.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'known for their cultural richness and vibrant arts') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Hey there! Can you find me the names and IDs of the top 5 medals that best represent the idea of winning an Olympic gold medal?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'best represent the idea of winning an Olympic gold medal') AS ref_vec_0\n\nSELECT id, medal_name, distance(medal.medal_description_embedding, ref_vec_0) AS distance \nFROM medal\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you show me a few cities that seem to be vibrant hubs filled with diverse cultures and have a rich history?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'vibrant hubs filled with diverse cultures and have a rich history') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Identify the five cities that best match the description of a bustling metropolitan area known for its culture and history, and provide their identifiers, names, descriptions, description embeddings, and similarity scores.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'bustling metropolitan city known for its culture and history') AS ref_vec_0\n\nSELECT city_name, city_description, city_description_embedding, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you please find the IDs and names of the two cities that best match the description of being vibrant and famous for cultural landmarks and rich history? I really need the top contenders!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'being vibrant and famous for cultural landmarks and rich history') AS ref_vec_0\n\nSELECT city.id, city.city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 2;" + }, + { + "question": "List the top 3 cities where the top 5 games related to the thrilling 2000 Summer Games with spectacular performances took place.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'thrilling 2000 Summer Games with spectacular performances') AS ref_vec_0,\n\nRecentGames AS (\n SELECT g.id, g.games_name, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM city c\nJOIN games_city gc ON toString(c.id) = toString(gc.city_id)\nJOIN RecentGames rg ON toString(gc.games_id) = toString(rg.id)\nORDER BY rg.distance\nLIMIT 3;" + }, + { + "question": "Hey! Can you tell me the name of the top city that's renowned for its historical vibe and stunning architecture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'renowned for its historical vibe and stunning architecture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "What is the name of the city that stands as a lone beacon, resonating with the enigmatic tales of London?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'stands as a lone beacon, resonating with the enigmatic tales of London') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\nFROM city AS c\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey, can you find me the top 5 regions within the National Olympic Committee that have a strong sporting heritage? I need their IDs and how closely they match that description!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'strong sporting heritage') AS ref_vec_0\n\nSELECT id, distance(noc_region.noc_region_description_embedding, ref_vec_0) AS distance\nFROM noc_region\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Can you provide the IDs and names of the top 5 cities that are recognized for their ancient architecture and historical significance?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'recognized for its ancient architecture and historical significance') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you list the games played in the top 5 cities that are known for their vibrant culture and historic landmarks, ordered by their similarity to these characteristics?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'are known for their vibrant culture and historic landmarks') AS ref_vec_0,\n\nSimilarCities AS (\n SELECT c.id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name\nFROM games AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN SimilarCities AS sc ON toString(gc.city_id) = toString(sc.id)\nORDER BY sc.distance;" + }, + { + "question": "Can you identify the top 5 cities that are bustling metropolitan areas known for their vibrant arts scenes and historic architecture, and give me their IDs?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'are bustling metropolitan areas known for their vibrant arts scenes and historic architecture') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "What are the names of the top 5 games related to the Olympic games held in a major city, and in which cities were they held?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic games held in a major city') AS ref_vec_0,\n\nRelevantGames AS (\n SELECT g.id AS game_id, g.games_name, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT rg.games_name, c.city_name\nFROM RelevantGames AS rg\nJOIN games_city AS gc ON toString(rg.game_id) = toString(gc.games_id)\nJOIN city AS c ON toString(gc.city_id) = toString(c.id)\nORDER BY rg.distance\nLIMIT 5;" + }, + { + "question": "Hey! Can you help me find the names of the top 5 Winter Games that happened in a major city? I'd like to know the names of the games and the cities they were held in. Make sure you get the ones that are closest to my description and just list the top 10 for me!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'happened in a major city') AS ref_vec_0,\n\ngames_with_distance AS (\n SELECT \n g.id AS games_id,\n g.games_name AS games_name,\n g.season AS season,\n g.games_year AS games_year,\n gc.city_id AS city_id,\n distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM \n games g\n JOIN \n games_city gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n games_with_distance.games_name AS games_name,\n c.city_name AS city_name\nFROM \n games_with_distance\nJOIN \n city c ON toString(games_with_distance.city_id) = toString(c.id)\nORDER BY \n games_with_distance.distance AS distance\nLIMIT 10;" + }, + { + "refine": "xxxxxxxxxx", + "question": "Seek out the names of cities that resemble shining jewels by the sea, famous for embracing the global tide of international events, alongside the names of games that radiate the warmth of a summer sports festival celebrated worldwide, and medals that embody the glory of supreme athletic achievement. Let me see who stands closest in this grand arena.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A famous coastal city known for hosting international events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Summer sports festival with global participation') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'A prestigious award given for top-tier athletic achievement') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medal_description_embedding, ref_vec_2) AS distance\n FROM medal\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n c.city_name AS city_name,\n g.games_name AS games_name,\n m.medal_name AS medal_name,\n c.distance as city_distance,\n g.distance as games_distance\n FROM c_filtered AS c\n JOIN \n games_city AS gc ON toString(c.id) = toString(gc.city_id)\n JOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\n JOIN \n competitor_event AS ce ON toString(g.id) = toString(ce.event_id)\n JOIN m_filtered AS m ON toString(ce.medal_id) = toString(m.id)\n ORDER BY \n c.distance AS distance\n LIMIT 10;" + }, + { + "question": "Could you please find the top 5 cities renowned for being bustling metropolises with rich cultural heritage and vibrant economies? I need their IDs, names, descriptions, and how closely they match this description!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'being bustling metropolises with rich cultural heritage and vibrant economies') AS ref_vec_0\n\nSELECT city_name, city_description, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Hey! Can you find me the top 5 cities that are vibrant, full of life, and have a deep cultural background? I'd love to know their names and a bit about them!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'are vibrant, full of life, and have a deep cultural background') AS ref_vec_0\n\nSELECT city_name, city_description, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Please find the ID of the city that best matches the description of being vibrant, with historic landmarks and cultural heritage.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'being vibrant, with historic landmarks and cultural heritage') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Can you find the game's ID and similarity score that best matches the description of \"The Summer Games of 2012\"?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Summer Games of 2012') AS ref_vec_0\n\nSELECT id, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Please list the names of the ten closest city and sports event pairs that match the descriptions of cities hosting international events with beautiful landscapes and major sports events attracting athletes from around the world.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'cities hosting international events with beautiful landscapes and major sports events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A major sports event attracting athletes worldwide') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarCities AS (\n SELECT \n c.id AS city_id,\n c.city_name AS city_name,\n c.distance AS distance\n FROM c_filtered AS c\n),\n\nSimilarGames AS (\n SELECT \n g.id AS game_id,\n g.games_name AS games_name,\n g.distance AS distance\n FROM g_filtered AS g\n)\n\nSELECT \n sc.city_name AS city_name,\n sg.games_name AS games_name\nFROM \n SimilarCities AS sc\nJOIN \n games_city AS gc ON toString(sc.city_id) = toString(gc.city_id)\nJOIN \n SimilarGames AS sg ON toString(sg.game_id) = toString(gc.games_id)\nORDER BY \n sc.distance, sg.distance\nLIMIT 10;" + }, + { + "question": "**\nCould you list the names of 10 cities known for their vibrant sporting events and cultural heritage, along with the names of games held there?\n**", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'vibrant sporting events and cultural heritage') AS ref_vec_0,\n\nCityMatch AS (\n SELECT c.id AS city_id, c.city_name, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 5\n),\n\nGamesInCity AS (\n SELECT g.id AS game_id, g.games_name, g.season, g.games_year, gc.city_id\n FROM games AS g\n JOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\n)\n\nSELECT cm.city_name, gic.games_name\nFROM CityMatch AS cm\nJOIN GamesInCity AS gic ON toString(cm.city_id) = toString(gic.city_id)\nORDER BY cm.distance\nLIMIT 10;" + }, + { + "question": "Could you tell me the names of the top 5 games that are associated with \"Summer games competition\" and the names of the cities that best fit \"City hosting international games\"? I need these based on the closest matches in terms of similarity.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer games competition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'City hosting international games') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 10\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 10\n),\n\nGameVectors AS (\n SELECT g.id AS game_id, g.games_name, gc.city_id, g.distance\n FROM g_filtered AS g\n JOIN games_city gc ON toString(g.id) = toString(gc.games_id)\n),\n\nCityVectors AS (\n SELECT c.id AS city_id, c.city_name, c.distance\n FROM c_filtered AS c\n)\n\nSELECT gv.games_name, cv.city_name\nFROM GameVectors gv\nJOIN CityVectors cv ON toString(gv.city_id) = toString(cv.city_id)\nORDER BY gv.distance + cv.distance\nLIMIT 5;" + }, + { + "question": "Identify the names of the top 2 games that are celebrated during the summer as annual sport festivals, and provide the names of the cities famous for hosting these events.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city famous for hosting the annual sport festival') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Annual sport festival celebrated in summer') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nCityGames AS (\n SELECT gc.games_id, c.id AS city_id, c.city_name, c.city_description_embedding, c.distance\n FROM c_filtered AS c\n JOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\n ORDER BY c.distance\n)\n\nSELECT g.games_name, cg.city_name\nFROM g_filtered AS g\nJOIN CityGames AS cg ON toString(g.id) = toString(cg.games_id)\nORDER BY g.distance\nLIMIT 2;" + }, + { + "question": "Hey there! Could you find me the top 5 cities where I can explore vibrant culture and historical landmarks? I'd love to know their names and how well they match this idea.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Explore the vibrant culture and historical landmarks of this bustling city') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Which Olympic Games were held in the city that stands as a beacon of being an \"Olympic host city\"?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic host city') AS ref_vec_0,\n\nCityMatch AS (\n SELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name\nFROM games g\nJOIN games_city gc ON toString(gc.games_id) = toString(g.id)\nJOIN CityMatch cm ON toString(cm.id) = toString(gc.city_id)\nORDER BY cm.distance\nLIMIT 1;" + }, + { + "question": "Which five cities shine brightest with a tapestry of cultural heritage and modern allure, and could be likened to bustling gems on the horizon?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its cultural heritage and modern attractions') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Can you tell me the name of the game linked to the city best known for its rich cultural vibrancy?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city renowned for its vibrant cultural heritage') AS ref_vec_0,\n\nRelevantCities AS (\n SELECT c.id AS city_id, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT g.games_name\nFROM games AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN RelevantCities AS rc ON toString(gc.city_id) = toString(rc.city_id)\nORDER BY rc.distance \nLIMIT 1;" + }, + { + "question": "Can you tell me the name of the city most associated with the 2012 Winter Games, which emphasizes snow sports and ice competitions, by finding the top match based on the description similarity?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The 2012 Winter Games held in Vancouver, focusing on snow sports and ice competitions') AS ref_vec_0,\n\nFilteredGames AS (\n SELECT id, games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\n FROM games\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.city_name\nFROM FilteredGames fg\nJOIN games_city gc ON toString(fg.id) = toString(gc.games_id)\nJOIN city c ON toString(gc.city_id) = toString(c.id)\nORDER BY fg.distance\nLIMIT 1;" + }, + { + "question": "Which games were most reminiscent of the summer sporting event in 2012, and what is their closeness in spirit?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer games held in 2012') AS ref_vec_0\n\nSELECT games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you tell me the names of the top 5 games that are described as exciting summer events and the names of the top 10 vibrant cities full of life where these games took place, ordered by their relevance?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Exciting summer event') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A vibrant city full of life') AS ref_vec_1,\n\ngames_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 10\n),\n\nMatchingGames AS (\n SELECT id, games_name, games_year, season\n FROM games_filtered AS games\n)\n\nSELECT c.city_name, mg.games_name\nFROM MatchingGames mg\nJOIN games_city gc ON toString(gc.games_id) = toString(mg.id)\nJOIN c_filtered AS c ON toString(c.id) = toString(gc.city_id)\nORDER BY c.distance;" + }, + { + "question": "Could you tell me which city is most recognized for hosting international sports events and is associated with a historic sports event in a global city?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for hosting international sports events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Historic sports event in a global city') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM c_filtered AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY c.distance\nLIMIT 1;" + }, + { + "question": "What are the IDs of the top three medals known for their prestigious first-place awards?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The medal awarded for first place is highly prestigious') AS ref_vec_0\n\nSELECT id, distance(medal.medal_description_embedding, ref_vec_0) AS distance\nFROM medal\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Could you find five cities that remind you of Barcelona because of their famous architecture and lively atmosphere? I'd like to know their names and see how closely related they are.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Barcelona is known for its architectural landmarks and vibrant culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "What are the names of the 3 cities hosting the top 5 games related to winter sports and thrilling competitions?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Winter sports and thrilling competitions') AS ref_vec_0,\n\nSimilarGames AS (\n SELECT g.id AS game_id, g.games_name, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN SimilarGames AS sg ON toString(gc.games_id) = toString(sg.game_id)\nORDER BY sg.distance\nLIMIT 3;" + }, + { + "question": "Identify the year, name, and similarity distance of the top three games most relevant to the description of the 1992 Summer Games held in Barcelona, particularly noted for their grand opening ceremony.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The 1992 Summer games held in Barcelona were noted for their grand opening ceremony') AS ref_vec_0\n\nSELECT games_year, games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Which five cities are the jewels of cultural richness and historical vibrancy in our bustling world?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan area known for its rich culture and history') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "** \nCould you tell me the name of the medal that most closely aligns with a \"Bronze medal with no available description\"? \n**", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Bronze medal with no available description') AS ref_vec_0\n\nSELECT medal_name, distance(medal.medal_description_embedding, ref_vec_0) AS distance \nFROM medal\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "**User**: \"I'm interested in finding out about Olympic Games.\"\n**Assistant**: \"Are you looking for information about games hosted by specific cities?\"\n**User**: \"Yes, specifically those cities that are typically known as Olympic Games host cities.\"\n**Assistant**: \"Alright, how many of these cities would you like us to focus on for finding the games?\"\n**User**: \"I'd like to consider the top 5 cities based on their description as host cities.\"\n**Assistant**: \"Understood. What details would you like about the games hosted in these cities?\"\n**User**: \"I need the names of these Olympic Games.\"\n**Assistant**: \"Great, I'll look up the names of the games hosted in the top 5 cities most associated with being Olympic host cities. Is there anything else you require?\"\n**User**: \"No, that should be all.\"\n**Assistant**: \"Okay, I'll retrieve the names of up to 10 games hosted by these cities, sorted by how closely the cities match that host city profile.\"", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic Games host city') AS ref_vec_0,\n\nCityMatch AS (\n SELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name\nFROM games g\nJOIN games_city gc ON toString(g.id) = toString(gc.games_id)\nJOIN CityMatch cm ON toString(gc.city_id) = toString(cm.id)\nORDER BY cm.distance\nLIMIT 10;" + }, + { + "question": "Hey! Can you show me the IDs and similarity scores for the top 5 cities that are really lively and famous for their cultural heritage and modern architecture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its cultural heritage and modern architecture') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Please identify the city ID for the city known for its many historical landmarks.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city with many historical landmarks') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey there! Can you tell me the names of the top 5 cities that are known for being lively and having historical landmarks?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling urban environment with historical landmarks') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Can you provide the IDs, names, and similarity distances of the top 3 cities renowned for their vibrant culture and historic architecture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for its vibrant culture and historic architecture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Could you show me the top 3 games that are associated with cities described as vibrant cultural hubs with historic architecture, focusing on major sporting events hosted in dynamic venues?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The vibrant cultural hub with historic architecture') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major sporting events hosted in dynamic venues') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarCities AS (\n SELECT c.id, c.city_name, c.city_description, c.distance\n FROM c_filtered AS c\n)\n\nSELECT g.games_name\nFROM SimilarCities sc\nJOIN games_city gc ON toString(sc.id) = toString(gc.city_id)\nJOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY g.distance\nLIMIT 3;" + }, + { + "question": "Identify the names of the top three cities that are most relevant to the description of an \"Olympic city with historic landmarks\".", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic city with historic landmarks') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance \nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Can you provide the name of the city that is the best match for being a vibrant coastal city with rich cultural heritage and modern attractions?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant coastal city with rich cultural heritage and modern attractions') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\nFROM city c\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey, could you help me find the top 10 pairs of cities and games that are most famous for their ties to historic Olympics and cultural significance? I'd love to know their names, starting from the most relevant ones!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city renowned for its historic Olympics') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Games are held in historic locations with significant cultural impact') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\ncity_match AS (\n SELECT c.id AS city_id, c.city_name, c.distance\n FROM c_filtered AS c\n),\n\ngames_match AS (\n SELECT g.id AS games_id, g.games_name, g.distance\n FROM g_filtered AS g\n)\n\nSELECT cm.city_name, gm.games_name\nFROM city_match cm\nJOIN games_city gc ON toString(cm.city_id) = toString(gc.city_id)\nJOIN games_match gm ON toString(gc.games_id) = toString(gm.games_id)\nORDER BY cm.distance, gm.distance\nLIMIT 10;" + }, + { + "question": "Can you identify the city that best represents the idea of a beautiful coastal city and provide its ID?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'beautiful coastal city') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Could you show me the IDs and names of the top 3 cities known for their vibrant arts scene and historical landmarks?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling city known for its vibrant arts scene and historical landmarks') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Hey! Can you help me find the top 3 cities that are bustling capitals with rich culture and history? I'd like to know their names, please!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling capital known for its rich culture and history') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\nFROM city c\nJOIN games_city gc ON toString(c.id) = toString(gc.city_id)\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Which city is hosting a game that's really close to an event with summer sports competitions?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer sports event with various competitions') AS ref_vec_0,\n\nRecentGames AS (\n SELECT g.id, g.games_name, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN RecentGames AS rg ON toString(gc.games_id) = toString(rg.id)\nORDER BY rg.distance\nLIMIT 1;" + }, + { + "question": "Top 5 cities known for vibrant culture. Give their names and distances.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan area known for its vibrant culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Can you identify the top 10 cities known for hosting historic sports events along with the corresponding international sports competitions with a long history that they hosted, including the games' names, years, and seasons?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling city known for hosting historic sports events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'International sports competition with a long history') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\ncity_knn AS (\n SELECT \n c.id AS city_id, \n c.city_name AS city_name, \n g.id AS games_id, \n g.games_name AS games_name, \n c.distance AS city_distance\n FROM c_filtered AS c\n JOIN \n games_city AS gc ON toString(c.id) = toString(gc.city_id)\n JOIN \n games AS g ON toString(gc.games_id) = toString(g.id)\n),\n\ngames_knn AS (\n SELECT \n g.id AS games_id, \n g.games_name AS games_name, \n g.games_year AS games_year, \n g.season AS season, \n g.distance AS games_distance\n FROM g_filtered AS g\n)\n\nSELECT \n ck.city_name AS city_name, \n gk.games_name AS games_name, \n gk.games_year AS games_year, \n gk.season AS season\nFROM \n city_knn AS ck\nJOIN \n games_knn AS gk ON toString(ck.games_id) = toString(gk.games_id)\nORDER BY \n ck.city_distance, gk.games_distance\nLIMIT 10;" + }, + { + "question": "Could you show me the top 5 regions that are most representative of major country's Olympic committees and provide their unique IDs and National Olympic Committee codes?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Olympic committee of a major country') AS ref_vec_0\n\nSELECT id, noc, distance(noc_region.noc_region_description_embedding, ref_vec_0) AS distance\nFROM noc_region\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "**User**: I'm interested in finding some cities.\n**Assistant**: What kind of cities are you looking for?\n**User**: Cities that are vibrant and known for their rich history and cultural landmarks.\n**Assistant**: How many cities would you like to discover?\n**User**: I'd like to find 3 cities.\n**Assistant**: Alright, I'll look for the 3 cities that best match your description. Is there anything specific you need to know about them?\n**User**: Yes, I want to know their names and descriptions.\n**Assistant**: Got it. I'll provide you with the names and descriptions of those 3 cities.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its rich history and cultural landmarks') AS ref_vec_0\n\nSELECT city_name, city_description, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Hey there! Could you find the top 5 cities that are famously known for hosting major sporting events? I'd love to know their names!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The vibrant city known for hosting major sporting events') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Can you tell me which game resembles that big international sporting event that happens every four years?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A major international multi-sport event held every four years') AS ref_vec_0\n\nSELECT games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey there! Could you find me the top 3 cities famous for hosting big-time Olympic events and tell me which games they hosted? I'm curious about the names of the cities, the games, and how closely they match the criteria.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A historic city known for hosting important Olympic events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Modern Olympics with global participation') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 2\n)\n\nSELECT c.city_name, g.games_name, c.distance\nFROM c_filtered AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY c.distance;" + }, + { + "question": "I want to find the top 5 cities that are famous tourist destinations known for their history and culture. Please provide their IDs and names.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Famous tourist destination known for its history and culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you provide the IDs and names of the top 5 cities that are renowned for their vibrant art and cultural scenes?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its art and culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "List the cities where the top 5 thrilling international athletic events occur.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling athletic event with international participation') AS ref_vec_0,\n\nSimilarGames AS (\n SELECT g.id, g.games_name, g.games_year, gc.city_id, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games g\n JOIN games_city gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM SimilarGames sg\nJOIN city c ON toString(sg.city_id) = toString(c.id)\nORDER BY sg.distance;" + }, + { + "question": "Please find the city that best matches the description of being a bustling metropolitan area known for its vibrant cultural scene and historical landmarks, and let me know the name. I need just one city that fits this description best!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan city known for its vibrant cultural scene and historical landmarks') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "What are the descriptions and distances of the top 3 cities known for frequently hosting international sports events?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city frequently hosting international sports events') AS ref_vec_0\n\nSELECT c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance \nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Could you please find the top 5 cities that are famous for their historic architecture and vibrant culture? I need the city names and how closely they match this description!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city known for its historic architecture and vibrant culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you show me the games' names and seasons for the top 3 cities renowned for historical architecture and vibrant culture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city renowned for its historical architecture and vibrant culture') AS ref_vec_0,\n\nCityVectorSearch AS (\n SELECT c.id AS city_id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT cvs.city_name, g.games_name, g.season\nFROM CityVectorSearch AS cvs\nJOIN games_city AS gc ON toString(cvs.city_id) = toString(gc.city_id)\nJOIN games AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY cvs.distance;" + }, + { + "question": "Can you provide the names of five cities known for their cultural diversity and vibrant economy, along with the names of games hosted in them? I am interested in the top matches based on their similarity to a bustling metropolitan area.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan area known for its cultural diversity and vibrant economy') AS ref_vec_0,\n\nCityMatch AS (\n SELECT c.id AS city_id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT cm.city_name, g.games_name\nFROM CityMatch AS cm\nJOIN games_city AS gc ON toString(cm.city_id) = toString(gc.city_id)\nJOIN games AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY cm.distance\nLIMIT 5;" + }, + { + "question": "Hey! Can you find me the game that matches up the best with the vibe of the 2002 Winter Olympics? I'd love to know its name!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Winter Olympics held in 2002') AS ref_vec_0\n\nSELECT games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "In the tapestry of global gatherings, reveal a duo of cities and games that twinkle with the essence of hosting grand international events and spirited sports competitions. Share their names and how closely they dance to this theme.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city known for hosting major international events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An international sports event with a wide range of competitions') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_cities AS (\n SELECT \n c.id AS city_id, \n c.city_name AS city_name, \n g.id AS games_id,\n c.distance AS city_distance\n FROM c_filtered AS c\n JOIN \n games_city AS gc ON toString(c.id) = toString(gc.city_id)\n JOIN \n games AS g ON toString(gc.games_id) = toString(g.id)\n ORDER BY \n c.distance AS distance\n),\n\nsimilar_games AS (\n SELECT \n g.id AS games_id,\n g.games_name AS games_name,\n g.distance AS games_distance\n FROM g_filtered AS g\n ORDER BY \n g.distance AS distance\n)\n\nSELECT \n sc.city_name AS city_name, \n sc.city_distance AS city_distance, \n sg.games_name AS games_name, \n sg.games_distance AS games_distance\nFROM \n similar_cities AS sc\nJOIN \n similar_games AS sg ON toString(sc.games_id) = toString(sg.games_id)\nLIMIT 10;" + }, + { + "question": "**User**: \"I'm interested in finding information about cities and sports events.\"\n**Assistant**: \"Could you specify what kind of cities you're looking for?\"\n**User**: \"I'm looking for historical cities known for hosting major international events.\"\n**Assistant**: \"How many such cities would you like information on?\"\n**User**: \"I'd like to find the top 5 cities.\"\n**Assistant**: \"Got it. Now, what kind of sports events are you interested in?\"\n**User**: \"I'm looking for international sports events that are held in the summer.\"\n**Assistant**: \"And how many of these events would you like to find?\"\n**User**: \"The top 5 as well.\"\n**Assistant**: \"Alright, I will look up the top 5 cities and the top 5 summer international sports events that match your descriptions. These will be ranked by their relevance to your interests. Is there anything else you need?\"\n**User**: \"No, that's all.\"\n**Assistant**: \"Okay, I will help you compile the information, including the names of the cities and events, the year and season of the events, and their relevance scores.\"", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A historical city known for hosting major international events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An international sports event held in summer') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nCityMatches AS (\n SELECT c.id AS city_id, c.city_name, c.distance AS city_distance\n FROM c_filtered AS c\n),\n\nGamesMatches AS (\n SELECT g.id AS games_id, g.games_name, g.games_year, g.season, g.distance AS games_distance\n FROM g_filtered AS g\n)\n\nSELECT cm.city_name, gm.games_name, gm.games_year, gm.season, cm.city_distance, gm.games_distance\nFROM CityMatches cm\nJOIN games_city gc ON toString(cm.city_id) = toString(gc.city_id)\nJOIN GamesMatches gm ON toString(gc.games_id) = toString(gm.games_id)\nORDER BY cm.city_distance, gm.games_distance\nLIMIT 10;" + }, + { + "question": "Could you tell me the IDs and similarity distances for the top 5 cities that are described as bustling metropolitan areas known for vibrant culture and historical landmarks?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan city known for its vibrant culture and historical landmarks') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "I am looking for the names, descriptions, and distances of the top 5 cities that most closely represent an iconic city known for its rich historical heritage and vibrant culture, ordered by their proximity of match.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An iconic city known for its rich historical heritage and vibrant culture') AS ref_vec_0\n\nSELECT city_name, city_description, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you tell me the name of a game that is located in one of the top 5 cities known for being vibrant and having historical significance along with modern amenities?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city with historical significance and modern amenities') AS ref_vec_0,\n\nTopCities AS (\n SELECT c.id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 5\n),\n\nGamesInCities AS (\n SELECT g.games_name\n FROM games g\n JOIN games_city gc ON toString(g.id) = toString(gc.games_id)\n WHERE gc.city_id IN (SELECT id FROM TopCities)\n)\n\nSELECT g.games_name\nFROM GamesInCities g\nLIMIT 1;" + }, + { + "question": "Identify the names of the games that were held in the top 5 cities most suitable for hosting major events, characterized as capital cities.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Capital city hosting major events') AS ref_vec_0,\n\nRelevantCities AS (\n SELECT c.id, c.city_name, c.city_description, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name\nFROM games AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN RelevantCities AS rc ON toString(rc.id) = toString(gc.city_id);" + }, + { + "question": "Could you show me the names and seasons of the top 10 games that are linked to the 5 cities most similar to the concept of Paris being a vibrant city with rich culture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Paris is a vibrant city with rich culture') AS ref_vec_0,\n\nCityMatches AS (\n SELECT c.id AS city_id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name, g.season\nFROM games g\nJOIN games_city gc ON toString(g.id) = toString(gc.games_id)\nJOIN CityMatches cm ON toString(gc.city_id) = toString(cm.city_id)\nORDER BY cm.distance\nLIMIT 10;" + }, + { + "question": "Hey there! Can you find me the top 10 games that are like summer competitions with multiple sports events and are held in cities known for hosting sports events? I'd love to know the names of these games and their cities!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer competition with multiple sports events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A metropolitan city hosting sports events') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarGames AS (\n SELECT\n g.id AS game_id, \n g.games_name AS games_name, \n gc.city_id AS city_id, \n g.distance AS game_distance\n FROM g_filtered AS g\n JOIN\n games_city gc ON toString(g.id) = toString(gc.games_id)\n),\n\nSimilarCities AS (\n SELECT\n c.id AS city_id, \n c.city_name AS city_name, \n c.distance AS city_distance\n FROM c_filtered AS c\n)\n\nSELECT \n sg.games_name AS games_name, \n sc.city_name AS city_name\nFROM \n SimilarGames sg\nJOIN \n SimilarCities sc ON toString(sg.city_id) = toString(sc.city_id)\nORDER BY \n sg.game_distance + sc.city_distance\nLIMIT 10;" + }, + { + "question": "Identify the top 5 cities renowned for hosting major sporting events and cultural festivals, and return the names of these cities, the games they host, and the similarity distance ranking.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city known for hosting major sporting events and cultural festivals') AS ref_vec_0,\n\nCityMatch AS (\n SELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT cm.city_name, g.games_name, cm.distance\nFROM CityMatch cm\nJOIN games_city gc ON toString(cm.id) = toString(gc.city_id)\nJOIN games g ON toString(gc.games_id) = toString(g.id)\nORDER BY cm.distance;" + }, + { + "question": "Hey, can you help me find out the top 5 cities that are most connected to the games with descriptions like \"Summer Games in 2012\"? I'm curious about their names!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer Games in 2012') AS ref_vec_0,\n\nGamesCTE AS (\n SELECT g.id, g.games_name, g.games_description, gc.city_id, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n JOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n),\n\nCityCTE AS (\n SELECT c.city_name, c.id, gc.distance\n FROM city AS c\n JOIN GamesCTE AS gc ON toString(c.id) = toString(gc.city_id)\n ORDER BY gc.distance\n LIMIT 5\n)\n\nSELECT CityCTE.city_name\nFROM CityCTE;" + }, + { + "question": "Amidst the whirlwind of thrilling summer sports spectacles, which city stands as the shining beacon where these exhilarating games unfold most vibrantly?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Summer Games with exciting sports events') AS ref_vec_0,\n\nFilteredGames AS (\n SELECT g.id, g.games_name, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN FilteredGames AS fg ON toString(gc.games_id) = toString(fg.id)\nORDER BY fg.distance\nLIMIT 1;" + }, + { + "question": "Find the five regions that resonate most with the spirit of 'USA', and share their names, codes, and how far they stray from this essence.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The NOC code ''''''''USA'''''''' represents the United States') AS ref_vec_0\n\nSELECT noc, region_name, distance(noc_region.noc_region_description_embedding, ref_vec_0) AS distance\nFROM noc_region\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you please find the names of games played in the top 5 cities that best embody the vibrant culture and history of Barcelona? Ensure the list is organized by similarity distance and shows no more than 10 game entries.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Barcelona is a vibrant city known for its rich culture and history') AS ref_vec_0,\n\nCityMatch AS (\n SELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name, c.city_name\nFROM games AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN CityMatch AS c ON toString(gc.city_id) = toString(c.id)\nORDER BY c.distance\nLIMIT 10;" + }, + { + "question": "Can you find a few cities that are well-known for hosting major sports events in summer and list the international games held there during the same season?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for hosting major sports events in summer') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'International sporting event held in summer') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name, g.games_name\nFROM c_filtered AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY c.distance, g.distance;" + }, + { + "question": "Identify the IDs of the five cities most representative of vibrant culture and historical landmarks.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A major city known for its vibrant culture and historical landmarks') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "**User**: I'm interested in finding some information about the Olympic Games.\n**Assistant**: What specific games are you looking for?\n**User**: I'm particularly interested in games similar to the summer games of 1992.\n**Assistant**: How many of these similar games would you like to explore?\n**User**: I'd like to see details about the top 3 games.\n**Assistant**: What additional details do you need about these games?\n**User**: I'd like to know the cities where these games took place and their names.\n**Assistant**: Do you have any preference for the order of the results?\n**User**: Please order them by relevance or similarity, and limit to 5 results.\n**Assistant**: Understood. I will generate the SQL query to retrieve the top 3 games similar to the summer games of 1992, and show their names along with the names of 5 cities where these games were held, sorted by relevance.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The summer games of 1992') AS ref_vec_0,\n\nGamesCTE AS (\n SELECT g.id AS games_id, g.games_name, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.city_name, gcte.games_name\nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN GamesCTE AS gcte ON toString(gc.games_id) = toString(gcte.games_id)\nORDER BY gcte.distance\nLIMIT 5;" + }, + { + "question": "Can you find the names of the cities that host the top 5 games most related to a thrilling sporting event in a vibrant city known for its culture and history? Please list the cities in order of their games' relevance to this description.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A thrilling sporting event held in a vibrant city known for its culture and history') AS ref_vec_0,\n\nGamesCTE AS (\n SELECT g.id, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM GamesCTE AS gc\nJOIN games_city AS gc_mapping ON toString(gc.id) = toString(gc_mapping.games_id)\nJOIN city AS c ON toString(gc_mapping.city_id) = toString(c.id)\nORDER BY gc.distance;" + }, + { + "question": "**User**: I'm interested in finding some city-related data.\n**Assistant**: Could you tell me what specific information you're looking for about these cities?\n**User**: I'm looking for cities known for hosting international events with historical significance.\n**Assistant**: How many such cities are you interested in discovering?\n**User**: I'd like the top 5 cities that fit this description.\n**Assistant**: All right. Along with city names, would you like information about any events associated with these cities?\n**User**: Yes, I'd like to know about major sports events held in these cities.\n**Assistant**: How many sporting events should I look for in these cities?\n**User**: Please find the top 5 events for each of these cities.\n**Assistant**: Got it. I will prepare a query that identifies the top 5 cities known for international events and the top 5 sports events associated with those cities.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A historical city known for international events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major sports events held in a renowned city') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name, g.games_name\nFROM c_filtered AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN g_filtered AS g ON toString(g.id) = toString(gc.games_id);" + }, + { + "question": "Hey, can you find me the top 5 games that are all about summer vibes and let me know which cities they happen in? I'm curious about the descriptions too!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'games occurring in the summer') AS ref_vec_0,\n\nGameSearch AS (\n SELECT \n g.id AS games_id, \n g.games_description AS games_description, \n distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM \n games g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n c.city_name AS city_name, \n gs.games_description AS games_description\nFROM \n city c\nJOIN \n games_city gc ON toString(c.id) = toString(gc.city_id)\nJOIN \n GameSearch gs ON toString(gc.games_id) = toString(gs.games_id)\nORDER BY \n gs.distance;" + }, + { + "question": "Identify the game most closely associated with Olympic events featuring exciting sports, and provide its identifier and similarity measure.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic games with exciting sporting events') AS ref_vec_0\n\nSELECT id, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "What is the ID of the region that aligns most closely with the idea of a national governing sports body and has connections to some of those older competitors?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The United States Olympic Committee is a national governing body for sports') AS ref_vec_0,\n\nCompetitorData AS (\n SELECT gc.id as competitor_id, gc.person_id, p.full_name, p.gender\n FROM games_competitor gc\n JOIN person p ON toString(gc.person_id) = toString(p.id)\n WHERE gc.age > 20\n),\n\nSimilarRegions AS (\n SELECT nr.id, nr.noc, nr.region_name, nr.noc_region_description, distance(nr.noc_region_description_embedding, ref_vec_0) AS distance\n FROM noc_region nr\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT sr.id\nFROM SimilarRegions sr\nJOIN person_region pr ON toString(sr.id) = toString(pr.region_id)\nJOIN CompetitorData cd ON toString(pr.person_id) = toString(cd.person_id)\nORDER BY sr.distance;" + }, + { + "question": "What are the IDs, NOC codes, names, and distances of the top 3 regions that are most closely related to the concept of being represented by the NOC code for Afghanistan?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The region represented by the NOC code is Afghanistan') AS ref_vec_0\n\nSELECT id, noc, region_name, distance(noc_region.noc_region_description_embedding, ref_vec_0) AS distance\nFROM noc_region\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Can you provide the names of the top 10 games held in the five cities most associated with Barcelona's historic architecture and art, sorted by city name and season?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Barcelona historic architecture and art') AS ref_vec_0,\n\nCityMatch AS (\n SELECT \n c.id AS city_id,\n c.city_name AS city_name,\n distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 5\n),\n\nGamesInCity AS (\n SELECT \n gc.games_id AS games_id,\n gm.games_name AS games_name,\n gm.season AS season,\n cm.city_name AS city_name\n FROM games_city AS gc\n JOIN games AS gm ON toString(gc.games_id) = toString(gm.id)\n JOIN CityMatch AS cm ON toString(gc.city_id) = toString(cm.city_id)\n)\n\nSELECT \n gi.games_name AS games_name\nFROM GamesInCity AS gi\nORDER BY gi.city_name, gi.season\nLIMIT 10;" + }, + { + "question": "Hey, can you help me find the cities that are famous for hosting big events and also have top summer games with high attendance taking place there? Let me know the names of those cities!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Games held in the summer with high attendance') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Cities known for hosting large events') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\nTopGames AS (\n SELECT g.id as game_id, g.games_name, g.distance\n FROM g_filtered AS g\n ORDER BY g.distance\n),\n\nTopCities AS (\n SELECT c.id as city_id, c.city_name, c.distance\n FROM c_filtered AS c\n ORDER BY c.distance\n)\n\nSELECT tc.city_name\nFROM TopGames tg\nJOIN games_city gc ON toString(tg.game_id) = toString(gc.games_id)\nJOIN TopCities tc ON toString(gc.city_id) = toString(tc.city_id);" + }, + { + "question": "Hey there! Could you find me the top 5 cities known for their awesome modern architecture and cultural heritage? And while you're at it, I want to know the names of games associated with these cities—just grab the top 10.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its modern architecture and cultural heritage') AS ref_vec_0,\n\nSimilarCities AS (\n SELECT \n c.id AS city_id,\n c.city_name AS city_name,\n distance(c.city_description_embedding, ref_vec_0) AS distance \n FROM \n city c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n sc.city_name AS city_name,\n g.games_name AS games_name,\n sc.distance AS distance \nFROM \n SimilarCities sc\nJOIN \n games_city gc ON toString(sc.city_id) = toString(gc.city_id)\nJOIN \n games g ON toString(gc.games_id) = toString(g.id)\nORDER BY \n sc.distance AS distance \nLIMIT 10;" + }, + { + "question": "What is the name of the game linked to the top city that matches the description of being vibrant with historical landmarks and cultural significance?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city with historical landmarks and cultural significance') AS ref_vec_0,\n\nSimilarCities AS (\n SELECT c.id AS city_id, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name\nFROM SimilarCities sc\nJOIN games_city gc ON toString(sc.city_id) = toString(gc.city_id)\nJOIN games g ON toString(gc.games_id) = toString(g.id)\nORDER BY sc.distance\nLIMIT 1;" + }, + { + "question": "Can you provide the IDs and city names of the top 5 games that are most similar to the \"Summer Games of 1992\", along with their associated similarity distances?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer Games of 1992') AS ref_vec_0,\n\nFilteredGames AS (\n SELECT g.id AS game_id, g.games_name, gc.city_id, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games g\n JOIN games_city gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT fg.game_id, c.city_name, fg.distance\nFROM FilteredGames fg\nJOIN city c ON toString(fg.city_id) = toString(c.id)\nORDER BY fg.distance;" + }, + { + "question": "Hey there! I'm really curious, could you help me find the top 5 games that have a vibe similar to memorable Summer Olympic events? I'd love to know what year they happened, their names, seasons, and a bit about them. Thanks!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer Olympic games with memorable events') AS ref_vec_0\n\nSELECT \n g.id AS id, \n g.games_year AS games_year, \n g.games_name AS games_name, \n g.season AS season, \n g.games_description AS games_description, \n distance(g.games_description_embedding, ref_vec_0) AS distance\nFROM \n games g\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "I need to find the game that best matches the description of the Summer Olympic Games held in 2021, focusing on innovations in sport technology, and provide its ID, name, and similarity score.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Summer Olympic Games held in 2021 featured innovations in sport technology') AS ref_vec_0\n\nSELECT id, games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey there! Could you find the top 5 games that represent a global sporting event with high participation? I'd love to know which historic cities with major international events host these games. Make sure to list them starting with the city closest to the concept.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Historic city with major international events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Global sporting event known for high participation') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nCityMatches AS (\n SELECT \n c.id AS city_id,\n c.city_name AS city_name,\n c.distance AS city_distance\n FROM c_filtered AS c\n)\n\nSELECT \n g.games_name AS games_name,\n cm.city_name AS city_name\nFROM g_filtered AS g\nJOIN \n games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN \n CityMatches AS cm ON toString(gc.city_id) = toString(cm.city_id)\nORDER BY \n cm.city_distance;" + }, + { + "question": "Find the IDs of the top 5 games that are related to winter sports events with snowy landscapes.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Winter sports event with snowy landscapes') AS ref_vec_0\n\nSELECT games.id, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "I would like to know the names of games that have been held in cities described as historical and known for hosting international events. Please provide the top 10 game names and their corresponding city names, focusing on the cities most relevant to this description.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A historical city known for hosting international events') AS ref_vec_0,\n\nCityMatches AS (\n SELECT \n id AS city_id, \n city_name,\n distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM \n city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n g.games_name AS games_name, \n c.city_name AS city_name\nFROM \n games g\nJOIN \n games_city gc ON toString(g.id) = toString(gc.games_id)\nJOIN \n CityMatches c ON toString(gc.city_id) = toString(c.city_id)\nORDER BY \n c.distance AS distance\nLIMIT 10;" + }, + { + "question": "Identify the names of the top 5 cities renowned for their historical significance and cultural heritage, and list them in order of relevance.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for its historical significance and cultural heritage') AS ref_vec_0,\n\nCityVectorSearch AS (\n SELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT cvs.city_name\nFROM CityVectorSearch cvs\nORDER BY cvs.distance;" + }, + { + "question": "Hey there! Could you find me the top 5 cities that are all about being vibrant with awesome history and culture? And then tell me what games were held there and what medals were won, in order of how closely they match this vibe!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city with a rich history and culture') AS ref_vec_0,\n\nSimilarCities AS (\n SELECT \n c.id AS city_id, \n c.city_name AS city_name, \n distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM \n city AS c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n sc.city_name AS city_name,\n g.games_name AS games_name,\n m.medal_name AS medal_name,\n sc.distance AS distance\nFROM\n SimilarCities AS sc\nJOIN\n games_city AS gc ON toString(sc.city_id) = toString(gc.city_id)\nJOIN\n games AS g ON toString(gc.games_id) = toString(g.id)\nJOIN\n competitor_event AS ce ON toString(g.id) = toString(ce.event_id)\nJOIN\n medal AS m ON toString(ce.medal_id) = toString(m.id)\nORDER BY\n sc.distance;" + }, + { + "question": "What are the cities associated with the top 3 games related to the 2024 Summer Olympics' athletic performances?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Summer Olympics of 2024 showcased remarkable athletic performances globally') AS ref_vec_0,\n\nSimilarGames AS (\n SELECT \n g.id AS game_id, \n distance(g.games_description_embedding, ref_vec_0) AS distance \n FROM \n games g\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT \n c.city_name AS city_name \nFROM \n SimilarGames sg\nJOIN \n games_city gc \nON toString(sg.game_id) = toString(gc.games_id)\nJOIN \n city c \nON toString(gc.city_id) = toString(c.id)\nORDER BY \n sg.distance;" + }, + { + "question": "What are the names and years of the top 5 Olympic games held in the 3 cities most known for their rich history and Olympic games?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its rich history and Olympic games') AS ref_vec_0,\n\nCitySearch AS (\n SELECT c.id AS city_id, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT g.games_name, g.games_year\nFROM games g\nJOIN games_city gc ON toString(g.id) = toString(gc.games_id)\nJOIN CitySearch cs ON toString(gc.city_id) = toString(cs.city_id)\nORDER BY cs.distance\nLIMIT 5;" + }, + { + "question": "Please provide the ID and similarity distance for the medal that best represents an award given to top athletes.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An award given to top athletes') AS ref_vec_0\n\nSELECT id, distance(medal.medal_description_embedding, ref_vec_0) AS distance\nFROM medal\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Please find and list the names of the top 5 cities known for their vibrant culture and historic landmarks, sorted by their similarity to this description.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The bustling metropolis is known for its vibrant culture and historic landmarks') AS ref_vec_0,\n\nCitySearch AS (\n SELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT city_name\nFROM CitySearch\nORDER BY distance;" + }, + { + "question": "Could you please identify the top 3 cities renowned for hosting international events, and that have been venues for games similar to Olympic summer games? I need their names, ordered by relevance.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Olympic Games in the summer') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A prominent city known for hosting international events') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 3\n),\n\nrelevant_games AS (\n SELECT g.id AS game_id\n FROM g_filtered AS g\n)\n\nSELECT c.city_name\nFROM c_filtered AS c\nJOIN games_city gc ON toString(c.id) = toString(gc.city_id)\nJOIN relevant_games rg ON toString(rg.game_id) = toString(gc.games_id)\nORDER BY c.distance;" + }, + { + "question": "Can you find a few games that remind you of the summer Olympics, and do you know which cities they might be linked to, especially those famous for hosting big international things?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Olympic games held in summer') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A city known for hosting international events') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name, c.city_name\nFROM g_filtered AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN c_filtered AS c ON toString(gc.city_id) = toString(c.id)\nORDER BY g.distance\nLIMIT 10;" + }, + { + "question": "Can you provide the names and descriptions of the top 3 cities renowned for their vibrant culture and historical landmarks?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling city known for its vibrant culture and historical landmarks') AS ref_vec_0\n\nSELECT city_name, city_description, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Hey there! Could you let me know which cities and games are considered the top 5 when it comes to hosting major sporting events? I'm curious about the international competitions happening in those cities. Thanks!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Major city known for hosting sporting events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'International sports competition') AS ref_vec_1,\n\ncity_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ngames_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nCityVectorSearch AS (\n SELECT city_name, distance\n FROM city_filtered AS city\n),\n\nGamesVectorSearch AS (\n SELECT id, games_name, distance\n FROM games_filtered AS games\n)\n\nSELECT c.city_name, g.games_name\nFROM CityVectorSearch c\nJOIN games_city gc ON toString(c.id) = toString(gc.city_id)\nJOIN GamesVectorSearch g ON toString(gc.games_id) = toString(g.id)\nORDER BY c.distance, g.distance;" + }, + { + "question": "Please provide the names of the top 10 games that are most closely related to \"Summer sports event with various athletic competitions\", along with the names of the cities they are associated with.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Summer sports event with various athletic competitions') AS ref_vec_0,\n\nRelevantGames AS (\n SELECT \n g.id AS game_id,\n g.games_name AS games_name,\n gc.city_id AS city_id,\n distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM \n games g\n JOIN \n games_city gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n rg.games_name AS games_name, \n c.city_name AS city_name\nFROM \n RelevantGames rg\nJOIN \n city c ON toString(rg.city_id) = toString(c.id)\nORDER BY \n rg.distance AS distance\nLIMIT 10;" + }, + { + "question": "Return the names of the top 5 games held in cities most similar to \"A bustling metropolitan area known for its sports events.\"", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolitan area known for its sports events') AS ref_vec_0,\n\nSimilarCities AS (\n SELECT c.id AS city_id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT g.games_name\nFROM games AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN SimilarCities AS sc ON toString(gc.city_id) = toString(sc.city_id)\nORDER BY sc.distance \nLIMIT 5;" + }, + { + "question": "Could you identify the games released after 2000 that are associated with the top 3 cities characterized by a modern urban landscape, and list these games and cities ordered by their relevance to this concept?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'modern urban landscape') AS ref_vec_0,\n\nRecentGames AS (\n SELECT g.id, g.games_name, g.games_description\n FROM games AS g\n WHERE g.games_year > 2000\n),\n\nCityVectorSearch AS (\n SELECT c.id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city AS c\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT rg.games_name, cvs.city_name\nFROM RecentGames AS rg\nJOIN games_city AS gc ON toString(rg.id) = toString(gc.games_id)\nJOIN CityVectorSearch AS cvs ON toString(gc.city_id) = toString(cvs.id)\nORDER BY cvs.distance;" + }, + { + "question": "What are the names of the games associated with the top 5 cities known for their historical landmarks and cultural significance?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city is known for its historical landmarks and cultural significance') AS ref_vec_0,\n\nsimilar_cities AS (\n SELECT c.id AS city_id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name, sc.city_name\nFROM similar_cities sc\nJOIN games_city gc ON toString(sc.city_id) = toString(gc.city_id)\nJOIN games g ON toString(gc.games_id) = toString(g.id)\nORDER BY sc.distance;" + }, + { + "question": "Identify the city that is recognized for having a vibrant culture and historical significance, and provide its ID and the similarity distance score.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for its vibrant culture and historical significance') AS ref_vec_0\n\nSELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "**User**: I'm interested in finding some cities.\n**Assistant**: What kind of cities are you looking for?\n**User**: Cities that have hosted significant historical events.\n**Assistant**: That's interesting! Are there any specific events or criteria you're considering?\n**User**: I'm thinking of cities that were involved in major sports competitions over the last decade.\n**Assistant**: I see. How many cities are you interested in, and should they be the top choices based on certain descriptions?\n**User**: I'd like to find the top 10 cities based on those criteria.\n**Assistant**: I'll help you find the top 10 cities that have hosted significant historical events and are related to sports competitions over the past decade, using the most relevant descriptions available.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A major city that has hosted historical events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Sport competitions held in the past decade') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name\nFROM c_filtered AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nJOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\nORDER BY c.distance, g.distance\nLIMIT 10;" + }, + { + "question": "Hey! Can you help me find the top 5 games that really capture the thrilling vibe of competitive sports and let me know which cities they're associated with?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The thrilling atmosphere of competitive sports') AS ref_vec_0,\n\nSimilarGames AS (\n SELECT \n g.id AS game_id,\n g.games_name AS games_name,\n gc.city_id AS city_id,\n distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM \n games g\n JOIN \n games_city gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n sg.games_name AS games_name, \n c.city_name AS city_name\nFROM \n SimilarGames sg\nJOIN \n city c ON toString(sg.city_id) = toString(c.id)\nORDER BY \n sg.distance AS distance\nLIMIT 5;" + }, + { + "question": "Can you tell me which games and cities are closely related to big summer sports events and famous sports-hosting cities? List some that stand out.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An international sports event held in the summer') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'A major city known for hosting sports events') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\nGameCitySimilarity AS (\n SELECT \n g.games_name AS games_name,\n c.city_name AS city_name,\n g.distance AS g_distance,\n c.distance AS c_distance\n FROM g_filtered AS g\n JOIN \n games_city AS gc ON toString(g.id) = toString(gc.games_id)\n JOIN c_filtered AS c ON toString(gc.city_id) = toString(c.id)\n)\n\nSELECT \n games_name,\n city_name\nFROM \n GameCitySimilarity\nORDER BY \n g_distance + c_distance\nLIMIT 10;" + }, + { + "question": "Find the city name of the most relevant city described as a bustling metropolis with an iconic skyline and vibrant culture.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis known for its iconic skyline and vibrant culture') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\nFROM city c\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Which city hosts the game that is most closely aligned with the idea of an exciting international athletic competition, considering the top 5 games based on their description and returning the city associated with the most similar game?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'An exciting international athletic competition') AS ref_vec_0,\n\nSimilarGames AS (\n SELECT\n g.id AS game_id,\n distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n c.city_name AS city_name\nFROM city c\nJOIN games_city gc ON toString(c.id) = toString(gc.city_id)\nJOIN SimilarGames sg ON toString(gc.games_id) = toString(sg.game_id)\nORDER BY sg.distance\nLIMIT 1;" + }, + { + "question": "Could you please locate the 5 cities where the top games, most related to the Summer Games held in 2012, took place? I need their names and the descriptions of these games, ordered by how closely they match the 2012 Summer Games description!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Summer Games held in 2012') AS ref_vec_0,\n\nFilteredGames AS (\n SELECT g.id, g.games_description, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.city_name, fg.games_description\nFROM FilteredGames AS fg\nJOIN games_city gc ON toString(fg.id) = toString(gc.games_id)\nJOIN city c ON toString(gc.city_id) = toString(c.id)\nORDER BY fg.distance\nLIMIT 5;" + }, + { + "question": "Embark on a journey to discover five cities that shine with historical significance and a rich tapestry of cultural heritage. What are their names, and how far does their light reach in terms of distance?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for its historical significance and cultural heritage') AS ref_vec_0\n\nSELECT c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\nFROM city AS c\nJOIN games_city AS gc ON toString(c.id) = toString(gc.city_id)\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "I am interested in identifying the names of cities and the medals they have awarded that are linked to top global sports events recognized for excellence. Specifically, I'm looking for the top 10 entries sorted by the distance associated with these games. Can you provide this information?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'City known for hosting sports events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'World-renowned sports event') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Award for excellence in sports') AS ref_vec_2,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(medal_description_embedding, ref_vec_2) AS distance\n FROM medal\n\n ORDER BY distance\n LIMIT 5\n),\n\nCityGameMedals AS (\n SELECT\n c.city_name AS city_name,\n g.games_name AS games_name,\n m.medal_name AS medal_name,\n g.distance AS games_distance\n FROM c_filtered AS c\n JOIN games_city gc ON toString(c.id) = toString(gc.city_id)\n JOIN g_filtered AS g ON toString(gc.games_id) = toString(g.id)\n JOIN competitor_event ce ON toString(ce.competitor_id) = toString(g.id)\n JOIN m_filtered AS m ON toString(ce.medal_id) = toString(m.id)\n ORDER BY g.distance\n)\n\nSELECT city_name, medal_name\nFROM CityGameMedals\nORDER BY games_distance\nLIMIT 10;" + }, + { + "question": "Could you show me the top city that best embodies the characteristics of Paris, known for its iconic landmarks and romantic atmosphere?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Paris is known for its iconic landmarks and romantic atmosphere') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\n WHERE city_name != 'Paris'\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Hey there! Could you help me find the top 5 regions that are famous for their historical Olympic achievements? I'm curious about their IDs!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The region known for its historical Olympic achievements') AS ref_vec_0\n\nSELECT id, distance(noc_region.noc_region_description_embedding, ref_vec_0) AS distance\nFROM noc_region\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "What is the ID, name, and distance of the city that is best known for hosting grand sports events and cultural diversity?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The city known for hosting grand sports events and cultural diversity') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Identify the name of the game that is hosted in a city most aligned with the description of being vibrant, rich in culture, and historically significant. Limit the search to the top five cities matching this description and select the game associated with the city having the closest match.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its rich culture and historical significance') AS ref_vec_0,\n\nCityMatch AS (\n SELECT id, distance(city.city_description_embedding, ref_vec_0) AS distance\n FROM city\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT g.games_name\nFROM games g\nJOIN games_city gc ON toString(g.id) = toString(gc.games_id)\nJOIN CityMatch cm ON toString(gc.city_id) = toString(cm.id)\nORDER BY cm.distance\nLIMIT 1;" + }, + { + "question": "In the realm where the summer sun dances with majestic ceremonies, seek out the cities that have witnessed such spectacular games. Which are the cities that have embraced the top 5 games akin to the grand Olympic festivities?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Olympic games held in summer with spectacular opening ceremonies') AS ref_vec_0,\n\nSimilarGames AS (\n SELECT g.id AS games_id, g.games_description, gc.city_id, distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM games AS g\n JOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 5\n),\n\nCityHosting AS (\n SELECT c.city_name, sg.games_description, sg.distance\n FROM city AS c\n JOIN SimilarGames AS sg ON toString(c.id) = toString(sg.city_id)\n ORDER BY sg.distance\n LIMIT 5\n)\n\nSELECT city_name\nFROM CityHosting;" + }, + { + "question": "Could you show me the ID and name of the game that most closely relates to the theme of a vibrant city hosting the Summer Games?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Summer Games were held in a vibrant city') AS ref_vec_0\n\nSELECT id, games_name, distance(games.games_description_embedding, ref_vec_0) AS distance\nFROM games\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Can you identify a few cities and games that might relate to a London feel and summer fun? Let's focus on their names and how they pair up.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'London description example') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Summer games example') AS ref_vec_1,\n\ncity_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ngames_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredCities AS (\n SELECT id AS city_id, city_name, distance AS city_distance\n FROM city_filtered AS city\n),\n\nFilteredGames AS (\n SELECT id AS games_id, games_name, distance AS games_distance\n FROM games_filtered AS games\n)\n\nSELECT c.city_name, g.games_name, c.city_distance + g.games_distance AS total_distance\nFROM FilteredCities c\nJOIN games_city gc ON toString(c.city_id) = toString(gc.city_id)\nJOIN FilteredGames g ON toString(gc.games_id) = toString(g.games_id)\nORDER BY total_distance\nLIMIT 10;" + }, + { + "question": "Identify the city that is both a major metropolitan area known for hosting global events and closely associated with international sports competitions held in the summer. Please provide the ID of this city.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'International sports competition held in the summer') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Major metropolitan area known for hosting global events') AS ref_vec_1,\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\nNearestGames AS (\n SELECT g.id AS games_id, g.games_name, g.games_year, g.season, g.distance\n FROM g_filtered AS g\n ORDER BY g.distance\n),\n\nGamesCities AS (\n SELECT gc.city_id\n FROM games_city gc\n JOIN NearestGames ng ON toString(gc.games_id) = toString(ng.games_id)\n),\n\nNearestCities AS (\n SELECT c.id AS city_id, c.city_name, c.distance\n FROM c_filtered AS c\n ORDER BY c.distance\n)\n\nSELECT nc.city_id\nFROM NearestCities nc\nJOIN GamesCities gc ON toString(nc.city_id) = toString(gc.city_id)\nORDER BY nc.distance\nLIMIT 1;" + }, + { + "question": "Find the top game related to a historic sporting event with modern international participation.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'historic sporting event in modern times with international participation') AS ref_vec_0,\n\nRelevantGames AS (\n SELECT \n g.id AS game_id,\n g.games_name AS games_name,\n gc.city_id AS city_id,\n distance(g.games_description_embedding, ref_vec_0) AS distance\n FROM \n games g\n JOIN \n games_city gc ON toString(g.id) = toString(gc.games_id)\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT \n rg.games_name AS games_name\nFROM \n RelevantGames rg\nJOIN \n city c ON toString(rg.city_id) = toString(c.id)\nORDER BY \n rg.distance AS distance\nLIMIT 1;" + }, + { + "question": "Reveal the identities and names of five cities that dance in the vibrant symphony of culture and history, closest to the heart of such bustling brilliance.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The bustling city known for its vibrant culture and rich history') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "Could you please find the top 5 vibrant coastal cities known for their cultural heritage and list the names and distances of the top 2 international multi-sport events celebrated every four years, associated with these cities?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant coastal city known for its cultural heritage') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'An international multi-sport event celebrated every four years') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_0) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 5\n),\n\ng_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_1) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 4\n),\n\nCityCTE AS (\n SELECT c.id AS city_id, c.city_name, c.distance AS city_distance\n FROM c_filtered AS c\n)\n\nSELECT g.games_name, g.distance AS games_distance\nFROM g_filtered AS g\nJOIN games_city AS gc ON toString(g.id) = toString(gc.games_id)\nJOIN CityCTE ON toString(CityCTE.city_id) = toString(gc.city_id)\nORDER BY g.distance\nLIMIT 2;" + }, + { + "question": "Hey there! Could you list out the top 5 cities that are famous for their cultural heritage and modern architecture? I need to know their IDs, names, and how close they are to this description.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A vibrant city known for its cultural heritage and modern architecture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance \nFROM city\nORDER BY distance\nLIMIT 5;" + }, + { + "question": "**User**: I'm interested in finding some cities to visit.\n**Assistant**: What kind of cities are you looking for?\n**User**: I'm looking for cities that are bustling and have a lot of culture and historical sites.\n**Assistant**: How many such cities would you like to find?\n**User**: Three would be perfect.\n**Assistant**: Great! I will find the top three cities that are known for being vibrant, cultural, and historical.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A bustling metropolis known for its vibrant culture and history') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "I would like to know the name of the city that best embodies the characteristics of Barcelona, being known for its vibrant atmosphere, architecture, and culture.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Barcelona is a vibrant city known for its architecture and culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 1;" + }, + { + "question": "Identify the top 3 cities renowned for their vibrant culture and historical landmarks, and provide their names along with their similarity distances.", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A city known for its vibrant culture and historical landmarks') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 3;" + }, + { + "question": "Could you show me the names of the games held in the year 2000 and the top 5 cities, similar to Sydney with its iconic Opera House and vibrant cultural scene, where these games took place?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Sydney is known for its iconic Opera House and vibrant cultural scene') AS ref_vec_0,\n\nGamesInYear AS (\n SELECT g.id AS games_id, g.games_name, g.games_year, gc.city_id\n FROM games g\n JOIN games_city gc ON toString(g.id) = toString(gc.games_id)\n WHERE g.games_year = 2000\n),\n\nCitySimilarity AS (\n SELECT c.id AS city_id, c.city_name, distance(c.city_description_embedding, ref_vec_0) AS distance\n FROM city c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ci.city_name, gsy.games_name\nFROM GamesInYear gsy\nJOIN CitySimilarity ci ON toString(gsy.city_id) = toString(ci.city_id)\nORDER BY ci.distance\nLIMIT 5;" + }, + { + "question": "Could you identify and list the names of 10 games most related to winter sports events and the names of 10 cities most related to European capitals, then show me the paired game and city names sorted by how closely they match these topics?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'winter sports events') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'European capitals') AS ref_vec_1,\n\ngames_filtered AS (\n SELECT\n *,\n distance(games_description_embedding, ref_vec_0) AS distance\n FROM games\n\n ORDER BY distance\n LIMIT 10\n),\n\ncity_filtered AS (\n SELECT\n *,\n distance(city_description_embedding, ref_vec_1) AS distance\n FROM city\n\n ORDER BY distance\n LIMIT 10\n),\n\nsimilar_games AS (\n SELECT id, games_name, distance\n FROM games_filtered AS games\n),\n\nsimilar_cities AS (\n SELECT city_name, distance\n FROM city_filtered AS city\n)\n\nSELECT g.games_name, c.city_name\nFROM similar_games g\nJOIN games_city gc ON toString(g.id) = toString(gc.games_id)\nJOIN similar_cities c ON toString(gc.city_id) = toString(c.id)\nORDER BY g.distance, c.distance\nLIMIT 10;" + }, + { + "question": "Hey! Could you help me find the top 3 cities that are famous for hosting international sporting events? I need their names!", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'A major city known for hosting international sporting events') AS ref_vec_0\n\nSELECT ci.city_name, distance(ci.city_description_embedding, ref_vec_0) AS distance\nFROM city AS ci\nORDER BY distance\nLIMIT 3;" + } +] \ No newline at end of file diff --git a/benchmark/data/results/test/synthea/candidate_sql.json b/benchmark/data/results/test/synthea/candidate_sql.json new file mode 100644 index 0000000..f769a18 --- /dev/null +++ b/benchmark/data/results/test/synthea/candidate_sql.json @@ -0,0 +1,3122 @@ +[ + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Cystitis') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the garden of ailments, where one seeks the tale of the bladder's lament, what is the most poignant narrative that captures the heart of Cystitis?", + "external_knowledge": "The `MATCH` operator is utilized for an approximate nearest neighbor (ANN) search, which finds items based on their semantic similarity. The `lembed()` function generates a vector representation of the term \"Cystitis\" using the 'all-MiniLM-L6-v2' model. The 'k = 1' clause ensures that only the single most similar description is returned. Vectors are compared using Euclidean distance (L2 norm), where a smaller distance indicates greater similarity. Understanding that Cystitis refers to bladder inflammation helps in interpreting the query's context.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Reason for acute bronchitis') AS ref_vec_0\n\nSELECT ID, distance(encounters.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a few encounters where the reason seems to be about acute bronchitis and give me their IDs?", + "external_knowledge": "The `MATCH` operator in this SQL query is used for approximate nearest neighbor (ANN) search, which is a technique that finds the closest matches in a dataset based on vector similarity. The `lembed()` function generates an embedding for the specified text using the model 'all-MiniLM-L6-v2'. The parameter `k = 3` specifies that the query should return the top 3 matches. In vector searches, similarity is typically determined using the Euclidean distance (L2 norm), where a smaller distance indicates a higher degree of similarity. The notion of \"reason seems to be about acute bronchitis\" is captured by comparing these vector embeddings.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Viral Sinusitis (Disorder)') AS ref_vec_0\n\nSELECT c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Can you please provide the descriptions of the top 5 conditions related to \"Viral Sinusitis (Disorder)\" for female patients?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic cough condition') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a description that might relate to a chronic cough situation?", + "external_knowledge": "In vector search operations, the `MATCH` operator is used to execute an approximate nearest neighbor (ANN) search, which identifies items that are semantically similar to a given input. The `lembed('all-MiniLM-L6-v2', ...)` function converts the input phrase into a vector representation using the specified language model. The search ranks potential matches based on proximity in the vector space, with closer matches indicating higher semantic similarity. In this context, \"a description that might relate to\" refers to finding the most relevant description based on these semantic similarities.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for overall health') AS ref_vec_0\n\nSELECT \n ID,\n DATE,\n PATIENT,\n CODE,\n DESCRIPTION,\n REASONCODE,\n REASONDESCRIPTION,\n distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 8, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the top 5 encounters related to a routine check-up for overall health? Please provide their IDs, dates, patient names, codes, descriptions, reason codes, reason descriptions, and similarity distances.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Encounter for symptom like fever') AS ref_vec_0,\n\nEncounterMatches AS (\n SELECT e.ID, e.PATIENT, e.DATE, e.DESCRIPTION, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters e\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT em.ID\nFROM EncounterMatches em\nJOIN patients p ON toString(em.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female' AND p.race = 'Asian'\nORDER BY em.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Can you list the top 10 encounters involving Asian female patients that are most similar to an encounter described as having symptoms like a fever?", + "external_knowledge": "", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine medical examination for healthy adult') AS ref_vec_0\n\nSELECT e.PATIENT, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Could you find a few female patients who recently underwent check-ups that are generally routine for healthy adults?", + "external_knowledge": "The \"MATCH\" operator is used in SQLite to perform an approximate nearest neighbor search, identifying how similar various data vectors are to a given vector representation. The function `lembed()` helps in generating these vector representations. In this context, the query retrieves up to 3 patients whose medical encounter descriptions are most similar to the concept of a \"Routine medical examination for healthy adult.\" The similarity search typically measures the distance between vectors, where a smaller distance indicates a higher similarity. This search is limited to female patients by filtering based on the gender column.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic bronchitis') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Bronchodilator') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM medications\n\n ORDER BY distance\n LIMIT 5\n),\n\nConditionMatches AS (\n SELECT \n c.PATIENT AS PATIENT, \n c.DESCRIPTION AS condition_description,\n c.distance AS condition_distance\n FROM c_filtered AS c\n ORDER BY \n c.distance AS distance\n),\n\nMedicationMatches AS (\n SELECT \n m.PATIENT AS PATIENT, \n m.DESCRIPTION AS medication_description,\n m.distance AS medication_distance\n FROM m_filtered AS m\n ORDER BY \n m.distance AS distance\n)\n\nSELECT \n e.PATIENT AS PATIENT\nFROM \n encounters e\nJOIN \n ConditionMatches cm ON toString(e.PATIENT) = toString(cm.PATIENT)\nJOIN \n MedicationMatches mm ON toString(e.PATIENT) = toString(mm.PATIENT)\nWHERE \n e.DATE BETWEEN '2023-01-01' AND '2023-12-31'\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you identify the patient who had encounters in 2023, was diagnosed with conditions most related to chronic bronchitis, and was prescribed medications most related to bronchodilators? Please limit the results to just one patient.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to peanuts') AS ref_vec_0\n\nSELECT PATIENT, DESCRIPTION, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the top 5 patients who have some kind of peanut allergy? I'd love to know their names and the details of their allergies.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Polio vaccine administered') AS ref_vec_0\n\nSELECT distance(immunizations.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM immunizations\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "What are the distances for the top few records closely associated with giving the polio vaccine?", + "external_knowledge": "In the context of vector operations:\n- The 'MATCH' operator performs an approximate nearest neighbor (ANN) search, identifying records that are closest in meaning to the specified vector representation.\n- The parameter 'k = 3' means that the query returns the top 3 most similar entries.\n- Vectors in this operation are compared using Euclidean distance (L2 norm), where smaller distances indicate higher similarity.\n- The query utilizes the 'lembed' function with the model 'all-MiniLM-L6-v2' to convert the text \"Polio vaccine administered\" into a vector for matching.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic respiratory disorder') AS ref_vec_0,\n\nConditionMatches AS (\n SELECT c.PATIENT, c.ENCOUNTER, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions AS c\n ORDER BY distance\n LIMIT 5\n),\n\nPatientInfo AS (\n SELECT pm.patient, pm.first, pm.last, cm.ENCOUNTER\n FROM patients pm\n JOIN ConditionMatches cm ON toString(pm.patient) = toString(cm.PATIENT)\n),\n\nEncounterDetails AS (\n SELECT ed.ID AS encounter_id, ed.DATE AS encounter_date, cm.distance\n FROM encounters ed\n JOIN ConditionMatches cm ON toString(ed.ID) = toString(cm.ENCOUNTER)\n ORDER BY cm.distance\n)\n\nSELECT pi.first || ' ' || pi.last AS full_name, ed.encounter_date\nFROM PatientInfo pi\nJOIN EncounterDetails ed ON toString(pi.ENCOUNTER) = toString(ed.encounter_id)\nORDER BY ed.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "List the full names and encounter dates for the top 10 patients related to chronic respiratory disorder.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease (disorder)') AS ref_vec_0\n\nSELECT p.first, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the first names of the top 3 patients whose medical conditions are most closely associated with chronic obstructive pulmonary disease?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Paracetamol for pain relief') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Chronic headache condition') AS ref_vec_1,\n\nm_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM medications\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\nMedicationMatch AS (\n SELECT \n m.PATIENT AS PATIENT, \n m.DESCRIPTION AS DESCRIPTION, \n m.distance AS distance\n FROM m_filtered AS m\n),\n\nConditionMatch AS (\n SELECT \n c.PATIENT AS PATIENT, \n c.DESCRIPTION AS DESCRIPTION\n FROM c_filtered AS c\n),\n\nJoinedData AS (\n SELECT \n mm.PATIENT AS PATIENT, \n cm.DESCRIPTION AS Condition_Description, \n mm.DESCRIPTION AS Medication_Description\n FROM \n MedicationMatch mm\n JOIN \n ConditionMatch cm ON toString(mm.PATIENT) = toString(cm.PATIENT)\n)\n\nSELECT \n (CAST(COUNT(DISTINCT jd.PATIENT) AS FLOAT) / (SELECT COUNT(*) FROM patients)) * 100 AS Prevalence_Percentage\nFROM \n JoinedData jd;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you tell me the percentage of patients who are prescribed medications similar to \"Paracetamol for pain relief\" and are diagnosed with conditions similar to \"Chronic headache condition\"?", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Recommendation to increase physical activity') AS ref_vec_0\n\nSELECT ID, DESCRIPTION, distance(careplans.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the top 3 care plans that talk about recommending more physical activity? I'd like to see their IDs and descriptions.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Hypertension condition') AS ref_vec_0\n\nSELECT distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nWHERE p.gender = 'F'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the distances of the top 5 conditions related to \"Hypertension condition\" for female patients, ordered by relevance?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_0,\n\nRelevantCarePlans AS (\n SELECT PATIENT, distance(careplans.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM careplans\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.patient\nFROM patients p\nJOIN RelevantCarePlans rcp ON toString(p.patient) = toString(rcp.PATIENT)\nORDER BY rcp.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the patients whose care plans are among the top 5 most related to respiratory therapy?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine medical examination') AS ref_vec_0\n\nSELECT p.DATE, p.DESCRIPTION, distance(p.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM procedures AS p\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Please identify the top 5 routine medical examination procedures and provide their dates and descriptions!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis disorder') AS ref_vec_0\n\nSELECT \n c.DESCRIPTION AS DESCRIPTION, \n p.race AS race, \n distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM \n conditions AS c\nJOIN \n patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Can you get me the details of the top 5 conditions most related to \"Acute bronchitis disorder\"? I need to know what they're called, the patient's race, and how closely related they are!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic condition management') AS ref_vec_0\n\nSELECT ID, distance(careplans.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you find me the ID of the care plan that's all about chronic condition management? Just the top one, please!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Viral Infection Example') AS ref_vec_0\n\nSELECT ITEM, PREVALENCE_RATE, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Unearth the top five health issues that echo the stormy whispers of a viral infection, and reveal their prevalence rates.", + "external_knowledge": "In vector operations, the `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, identifying items whose vector embeddings closely align with a specified query vector. The `lembed()` function generates vector embeddings based on input phrases or concepts, in this case, \"Viral Infection Example\". The ANN search compares these embeddings using Euclidean distance (L2 norm), where smaller distances indicate higher similarity. The LIMIT clause specifies the number of results to return, here capped at five. Additionally, \"Viral Infection\" refers to diseases caused by viruses, characterized by high transmissibility and diverse symptoms.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute respiratory conditions') AS ref_vec_0\n\nSELECT ID, distance(encounters.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm trying to find some specific medical encounters.\n**Assistant**: What kind of medical encounters are you looking for?\n**User**: I'm interested in those related to acute respiratory conditions.\n**Assistant**: How many encounters would you like information on?\n**User**: Just find the one that is most relevant or representative.\n**Assistant**: Alright, I'll look for the single encounter that best matches acute respiratory conditions.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n\nEncounterReasons AS (\n SELECT ID, PATIENT, distance(encounters.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.PATIENT\nFROM EncounterReasons er\nJOIN encounters e ON toString(er.ID) = toString(e.ID)\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.race = 'White'\nORDER BY er.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you identify the top white patient who had an encounter most related to \"Acute bronchitis (disorder)\"?", + "external_knowledge": "", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Comprehensive diabetes care plan') AS ref_vec_0,\n\npatient_info AS (\n SELECT \n patient, \n first, \n last, \n gender, \n ethnicity\n FROM \n patients\n WHERE \n gender = 'Female' AND\n ethnicity = 'Hispanic or Latino'\n)\n\nSELECT \n cp.ID AS ID, \n cp.DESCRIPTION AS DESCRIPTION, \n p.first AS first, \n p.last AS last, \n distance(cp.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM \n careplans AS cp\nJOIN \n patient_info AS p ON toString(cp.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please gather the details of the top 10 care plans that are most relevant to a comprehensive diabetes care plan? I need the care plan IDs, descriptions, and the names of the female Hispanic or Latino patients associated with these plans, ensuring you include the distance measurement indicating relevance.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic bronchitis') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Care plan for respiratory condition') AS ref_vec_1,\n\nconditions_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 10\n),\n\ncp_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 5\n),\n\nBronchitisConditions AS (\n SELECT PATIENT, DESCRIPTION, distance\n FROM conditions_filtered AS conditions\n)\n\nSELECT cp.DESCRIPTION, c.DESCRIPTION AS ConditionDescription, c.distance\nFROM BronchitisConditions c\nJOIN cp_filtered AS cp ON toString(c.PATIENT) = toString(cp.PATIENT)\nORDER BY c.distance;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify and list the care plan descriptions and condition descriptions for patients with a history of chronic bronchitis and a respiratory-related care plan, ordered by the degree of relevance to the chronic bronchitis condition.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'pregnancy conditions') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelatedProcedures AS (\n SELECT p.PATIENT, p.DATE, p.DESCRIPTION, p.distance\n FROM p_filtered AS p\n)\n\nSELECT c.DESCRIPTION\nFROM RelatedProcedures rp\nJOIN c_filtered AS c ON toString(rp.PATIENT) = toString(c.PATIENT)\nORDER BY c.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Find the top condition description related to pregnancy for patients who underwent a standard pregnancy test procedure.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function') AS ref_vec_0\n\nSELECT p.PATIENT, p.DESCRIPTION, distance(p.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures p\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Find the top 5 procedures related to measuring respiratory function, and return the patients and their descriptions.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Bacterial Sinusitis Condition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Chronic bronchitis disorder') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ncp_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.birthplace\nFROM patients p\nJOIN c_filtered AS c ON toString(p.patient) = toString(c.PATIENT)\nJOIN cp_filtered AS cp ON toString(p.patient) = toString(cp.PATIENT);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you identify the birthplaces of patients who are among the top 5 most related to having a condition described as \"Bacterial Sinusitis\" and also have careplans reasons among the top 5 most related to \"Chronic bronchitis disorder\"?", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic disease management plan focusing on diabetes control') AS ref_vec_0,\n\nTopCarePlans AS (\n SELECT \n ID, \n PATIENT, \n distance(careplans.DESCRIPTION_embedding, ref_vec_0) AS distance \n FROM \n careplans\n ORDER BY distance\n LIMIT 5\n),\n\nMedicationRecords AS (\n SELECT \n PATIENT, \n DESCRIPTION \n FROM \n medications \n WHERE \n PATIENT IN (SELECT PATIENT FROM TopCarePlans)\n)\n\nSELECT \n m.DESCRIPTION AS DESCRIPTION\nFROM \n MedicationRecords m\nJOIN \n TopCarePlans t ON toString(m.PATIENT) = toString(t.PATIENT)\nORDER BY \n t.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 23, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the vast forest of patient care, unveil the medication pathways prescribed to the five patients walking the focused path of chronic disease management, specifically taming the diabetes beast.", + "external_knowledge": "The `MATCH` operator in SQLite's vector extension performs an approximate nearest neighbor (ANN) search to find entries most similar to a specified vector representation. The `lembed()` function generates a vector embedding for a provided text, and the `k = 5` condition specifies that only the top 5 closest matches should be returned. In vector searches, the similarity is determined by distance (most commonly, Euclidean distance), where a smaller distance indicates higher similarity. In this context, a \"Chronic disease management plan focusing on diabetes control\" is the vector target, seeking care plans aligned with managing diabetes effectively.", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'pain relief medication') AS ref_vec_0\n\nSELECT START, STOP, PATIENT, distance(medications.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Could you tell me about a few cases of patients who have been on medications typically meant to relieve pain, including when those medications started and ended?", + "external_knowledge": "The MATCH operator in SQLite is being used here for a vector similarity search. It performs approximate nearest neighbor (ANN) searches, which find entries most similar to a specified vector. In this context, up to 5 medications are being retrieved that are closest to the vector representation of \"pain relief medication.\" The 'all-MiniLM-L6-v2' model provides a way to embed text into vectors, allowing semantic comparisons. This model typically uses Euclidean distance (L2 norm) for comparing vectors, meaning similarity increases as the distance between vectors decreases.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for general health') AS ref_vec_0\n\nSELECT ID, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the IDs of the top 5 encounters that are most relevant to a routine check-up for general health.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Pain') AS ref_vec_0\n\nSELECT c.DESCRIPTION, p.first, p.last, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you tell me about a few cases that are likely related to Chronic Pain, along with the names of the people involved?", + "external_knowledge": "The `MATCH` operator in the query is utilized to perform an approximate nearest neighbor (ANN) search, which helps in identifying similar items based on their vector embeddings. The parameter `k=5` specifies that the query should return the top 5 most similar conditions. The similarity is assessed using Euclidean distance in the vector space, where a smaller distance indicates a higher similarity. The `lembed` function translates a text phrase into its vector representation, which in this case is \"Chronic Pain\" using the `all-MiniLM-L6-v2` model, a compact and efficient transformer model for embedding generation. In this context, \"a few cases\" refers to these top 5 conditions that are found to be most similar to \"Chronic Pain\".", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic sinusitis with nasal polyps') AS ref_vec_0\n\nSELECT START, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find some conditions that are closely related to having chronic sinus issues with nasal polyps, and tell me when they start?", + "external_knowledge": "The `MATCH` operator in this context is performing an approximate nearest neighbor (ANN) search, which is a common technique for finding the closest matches to a given vector. The `lembed` function creates an embedding vector from the text \"Chronic sinusitis with nasal polyps\" using the 'all-MiniLM-L6-v2' model. This vector is then compared against the embeddings in the \"DESCRIPTION_embedding\" column to identify the top 5 most similar records. The similarity is calculated based on the Euclidean distance (L2 norm), with smaller distances indicating higher similarity.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Recommendation to limit physical activity') AS ref_vec_0\n\nSELECT p.first, p.last, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the first and last names of the patients along with the top 5 care plans that include recommendations to limit physical activity?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Bronchitis') AS ref_vec_0\n\nSELECT p.first, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in finding some patient information.\n**Assistant**: What specific patient information are you looking to retrieve?\n**User**: I'm looking for patients with conditions related to a specific illness.\n**Assistant**: Which illness are you interested in?\n**User**: Bronchitis.\n**Assistant**: How many patients would you like to find who have conditions related to Bronchitis?\n**User**: I'd like to find the top 5 patients.\n**Assistant**: Sure, I will help you get the names of the top 5 patients whose conditions are most related to Bronchitis using the semantic search capabilities.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine physical examination') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the top 5 procedures related to a routine physical examination?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up') AS ref_vec_0\n\nSELECT distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance, p.birthdate \nFROM encounters AS e\nJOIN patients AS p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Find the birthdates of patients involved in the top 5 most related encounters to a \"Routine check-up\", sorted by similarity.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Viral Sinusitis (Disorder)') AS ref_vec_0\n\nSELECT p.patient, p.first, p.last, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM patients p\nJOIN conditions c ON toString(p.patient) = toString(c.PATIENT)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you provide the patient IDs, first names, and last names of the top 5 patients diagnosed with conditions most similar to \"Viral Sinusitis (Disorder),\" ordered by their similarity?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Aspirin used for pain relief') AS ref_vec_0\n\nSELECT p.first, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications m\nJOIN patients p ON toString(m.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the first names of the 5 patients most relevant to the use of Aspirin for pain relief?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of blood pressure (procedure)') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "** \nCould you provide descriptions and distances for a handful of procedures related to checking blood pressure? \n**", + "external_knowledge": "** \nThe `MATCH` operator in vector searches is used to perform an approximate nearest neighbor (ANN) search, aiming to identify items that are semantically similar based on their embeddings. The `lembed('all-MiniLM-L6-v2', ...)` function generates vector embeddings using the 'all-MiniLM-L6-v2' model, a language model that transforms text data into vectors for similarity comparison. The parameter `k = 5` specifies that the query returns the top 5 entries that are most similar based on Euclidean distance (L2 norm), where smaller distances indicate greater similarity. Understanding blood pressure measurement procedures as a concept helps in identifying relevant medical practices. \n**", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease (disorder)') AS ref_vec_0\n\nSELECT distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Return the distance value for the top entry most associated with chronic obstructive pulmonary disease from the conditions database.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function') AS ref_vec_0\n\nSELECT p.patient, c.DESCRIPTION, c.START, c.STOP, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Can you identify the five patients with conditions most closely related to the measurement of respiratory function, and provide details such as the condition descriptions, start and stop dates, ordered by their similarity?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0,\n\nAlivePatients AS (\n SELECT patient, gender, birthplace\n FROM patients\n WHERE deathdate IS NULL\n AND birthplace LIKE '%Springfield%'\n)\n\nSELECT p.DESCRIPTION, ap.gender, ap.birthplace, distance(p.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures p\nJOIN AlivePatients ap ON toString(p.PATIENT) = toString(ap.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I'm interested in knowing more about medical procedures carried out on patients.\"\n**Assistant**: \"What kind of procedures are you interested in finding information about?\"\n**User**: \"I'm looking for procedures that are similar to a 'Standard pregnancy test'.\"\n**Assistant**: \"How many such procedures would you like to identify?\"\n**User**: \"I'd like to find the top 5 procedures.\"\n**Assistant**: \"Is there a specific group of patients you are focusing on?\"\n**User**: \"Yes, I'm interested in procedures for patients who were born in Springfield and are still alive.\"\n**Assistant**: \"Got it. Do you need any specific details about these procedures?\"\n**User**: \"I'd like to know the description, and also the gender and birthplace of the patients involved.\"\n**Assistant**: \"Is there anything else you need?\"\n**User**: \"No, that's all for now.\"\n**Assistant**: \"Alright, I will help you translate your request into an SQL query to find the top 5 procedures most relevant to a 'Standard pregnancy test' for living patients born in Springfield, including their description, gender, and birthplace.\"", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common cold prevalence analysis') AS ref_vec_0\n\nSELECT ap.ITEM, e.DATE, distance(ap.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences ap\nJOIN encounters e ON toString(ap.ITEM) = toString(e.DESCRIPTION)\nWHERE e.DATE > '2023-01-01'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the top 5 items related to \"Common cold prevalence analysis,\" with encounter dates after January 1, 2023?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Assessment of cough remedy effectiveness') AS ref_vec_0,\n\nRankedEncounters AS (\n SELECT \n e.PATIENT AS PATIENT,\n distance(e.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM \n encounters e\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.first\nFROM RankedEncounters re\nJOIN patients p ON toString(re.PATIENT) = toString(p.patient)\nORDER BY re.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "What is the first name of the patient whose encounter is most closely related to evaluating the effectiveness of a cough remedy, specifically among the top 5 encounters?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Obstructive Pulmonary Disease') AS ref_vec_0,\n\nPatientInfo AS (\n SELECT patient, birthdate\n FROM patients\n)\n\nSELECT c.PATIENT, p.birthdate, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN PatientInfo AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you identify the top 5 patients who are most associated with Chronic Obstructive Pulmonary Disease, and provide their birthdates?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_0\n\nSELECT p.first, p.last, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the first and last names, along with the relevance scores, for the top 5 female patients whose care plans are most pertinent to \"Respiratory therapy\"?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function') AS ref_vec_0\n\nSELECT e.ID AS encounter_id, p.first AS patient_first_name, p.birthdate, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Can you unearth the top 5 patient stories where the essence of understanding breath has been captured? Provide their encounter IDs, first names, birthdates, and the measure of their closeness to the breath of life study.", + "external_knowledge": "The query leverages a vector-based search using embeddings, specifically with the `MATCH` operator for approximate nearest neighbor (ANN) search. In this context, \"k=5\" instructs the query to return the top 5 closest matches to the conceptual query \"Measurement of respiratory function.\" These matches are determined through their Euclidean distance in the embedding space, with smaller distances indicating greater similarity. The embedding model, `all-MiniLM-L6-v2`, transforms textual descriptions into a vectorized format, allowing for this form of semantic search. External knowledge implies that the metaphorical \"breath of life\" relates to respiratory functions, emphasizing the life-giving aspect of breathing.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Recommendation to avoid exercise') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Viral Sinusitis (Disorder)') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n e.ID AS encounter_id,\n c.DESCRIPTION AS condition_description\nFROM e_filtered AS e\nJOIN c_filtered AS c ON toString(e.PATIENT) = toString(c.PATIENT)\nORDER BY \n e.distance, c.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find a few encounters where people are advised to skip exercise, particularly when they have something like a sinus issue?", + "external_knowledge": "- The `MATCH` operator is used for approximate nearest neighbor (ANN) search, which finds items most similar to the given criteria.\n- The phrase \"a few\" is interpreted through vector similarity search, limiting results to the top 5 matches (`k=5`).\n- The `lembed` function generates vector embeddings using the specified model (`all-MiniLM-L6-v2`) to capture semantic meaning.\n- Similarity is determined based on Euclidean distance (L2 norm), with lower distances indicating greater similarity.\n- \"Recommendation to avoid exercise\" is a vague description prompting the search for related encounter descriptions, while \"Viral Sinusitis (Disorder)\" specifies the condition of interest for filtering conditions.", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in finding a procedure related to respiratory function.\n**Assistant**: Can you specify what aspect of respiratory function you're interested in?\n**User**: I'm specifically looking for something about measuring respiratory function.\n**Assistant**: Got it. How many procedures would you like to find?\n**User**: Just the most relevant one, please.\n**Assistant**: Alright, I'll look for the procedure description that best matches 'Measurement of respiratory function'.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0\n\nSELECT p.first, p.last, p.gender, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Can you help me out by finding the top 5 patients who have conditions most like acute bronchitis? I'd love to know their first and last names, gender, and the distance of similarity. Thanks!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function (procedure)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function (procedure)') AS ref_vec_1,\n\npr_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 1\n),\n\npro_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 1\n),\n\nPatientInfo AS (\n SELECT p.patient, p.first, p.last, p.gender, p.birthdate\n FROM patients p\n JOIN pr_filtered AS pr ON toString(p.patient) = toString(pr.PATIENT)\n)\n\nSELECT pi.patient\nFROM PatientInfo pi\nJOIN pro_filtered AS pro ON toString(pi.patient) = toString(pro.PATIENT)\nORDER BY pro.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Seek out the identities of the souls who have embarked on the journey of measuring their breath's dance among the stars, guided by the virtue of closest similarity. Who are these chosen ones?", + "external_knowledge": "In this context, the `MATCH` operator is used to perform approximate nearest neighbor (ANN) searches, which are efficient methods for finding vectors similar to a given query vector. The `lembed('all-MiniLM-L6-v2', \"Measurement of respiratory function (procedure)\")` indicates the transformation of the textual description into a vector using the specified language model, capturing semantic meaning. The operation `k = 1` specifies returning the single closest match in terms of vector similarity, measured by Euclidean distance (L2 norm), where a smaller distance implies greater similarity. This is particularly useful in scenarios where semantics rather than exact string matches are desired, such as identifying procedures akin to respiratory function measurement.", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis treatment') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Bronchitis procedure') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT e.ID AS EncounterID, p.distance AS ProcedureDistance\nFROM e_filtered AS e\nJOIN p_filtered AS p ON toString(e.PATIENT) = toString(p.PATIENT)\nORDER BY p.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the 10 encounters that are most related to the treatment of acute bronchitis, along with the similarity distances of their procedures to bronchitis procedures?", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to peanuts and tree nuts') AS ref_vec_0\n\nSELECT START, PATIENT, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in finding out more about patients with certain allergies.\n**Assistant**: Which specific allergies are you interested in?\n**User**: I'm looking for information on allergies related to peanuts and tree nuts.\n**Assistant**: How many records would you like to retrieve for these allergies?\n**User**: I'd like to see the top 3 records.\n**Assistant**: I will search for the 3 allergies most representative of a peanut and tree nut allergy. Is there any specific information you need about these records?\n**User**: Yes, I'd like to know when these allergies were recorded and the patient identifiers.\n**Assistant**: Okay, I will also include a measure of how closely each record matches the allergy description. Is there anything else you require?\n**User**: No, that's all I need.\n**Assistant**: Great, I'll proceed with finding this information for you.", + "external_knowledge": "", + "integration_level": 2, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for seasonal health') AS ref_vec_0\n\nSELECT e.ID, p.first, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "List the IDs and first names of patients involved in the top 5 routine check-ups for seasonal health.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up visit for hypertension') AS ref_vec_0,\n\nEncounterMatches AS (\n SELECT e.ID, e.DATE, e.PATIENT, e.DESCRIPTION, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters AS e\n ORDER BY distance\n LIMIT 10\n),\n\nConditionPrevalence AS (\n SELECT c.PATIENT, c.ENCOUNTER, c.CODE, c.DESCRIPTION\n FROM conditions AS c\n JOIN all_prevalences AS p ON toString(c.CODE) = toString(p.ITEM) \n WHERE p.POPULATION_TYPE = 'Hypertension Patients'\n AND p.PREVALENCE_PERCENTAGE > 5\n),\n\nPatientInfo AS (\n SELECT p.patient, p.first, p.last, p.race, p.ethnicity, p.gender\n FROM patients AS p\n WHERE p.race = 'White' AND p.ethnicity = 'Not Hispanic or Latino'\n)\n\nSELECT em.ID AS EncounterID, em.DATE AS EncounterDate, em.DESCRIPTION AS EncounterDescription, \n cp.CODE AS ConditionCode, cp.DESCRIPTION AS ConditionDescription, \n pi.first AS FirstName, pi.last AS LastName, pi.race, pi.ethnicity, pi.gender, em.distance\nFROM EncounterMatches AS em\nJOIN ConditionPrevalence AS cp ON toString(em.PATIENT) = toString(cp.PATIENT)\nJOIN PatientInfo AS pi ON toString(em.PATIENT) = toString(pi.patient)\nORDER BY em.distance\nLIMIT 5;", + "sql_result_column_count": 11, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the top 5 routine check-up encounters for hypertension, specifically for White patients who are not Hispanic or Latino. Include details such as the encounter ID, date, description, associated condition code and description, patient’s first and last name, race, ethnicity, gender, and the similarity distance, ensuring that the encountered conditions have a prevalence greater than 5% among hypertension patients.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Urological problems like cystitis') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarConditions AS (\n SELECT c.PATIENT, c.DESCRIPTION, c.START, e.REASONDESCRIPTION, e.DATE, c.distance\n FROM c_filtered AS c\n JOIN e_filtered AS e ON toString(c.ENCOUNTER) = toString(e.ID)\n)\n\nSELECT PATIENT\nFROM SimilarConditions\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the top 10 patients who are dealing with conditions similar to urological issues like cystitis and also had encounters due to acute bronchitis? I'd love to know their IDs sorted by how closely they match!", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic pain due to injury') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Physical therapy for recovery') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 3\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT p.first\nFROM patients p\nJOIN c_filtered AS c ON toString(p.patient) = toString(c.PATIENT)\nJOIN e_filtered AS e ON toString(p.patient) = toString(e.PATIENT)\nORDER BY c.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I need a list of first names of patients whose medical conditions are described as \"Chronic pain due to injury\" and whose encounters are related to \"Physical therapy for recovery\". Please only include the top 3 matches for each description, sorted by how closely their condition matches the description.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'medication for viral sinusitis') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(medications.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 medications specifically used for treating viral sinusitis and provide their descriptions?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Influenza (Disorder)') AS ref_vec_0\n\nSELECT a.PREVALENCE_RATE, distance(a.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences AS a\nJOIN patients AS p ON toString(a.POPULATION_TYPE) = toString(p.race)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to know the prevalence rates of the disorder that is most closely associated with \"Influenza\" across different racial groups. Can you provide the top 5 matches sorted by their relevance or similarity?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Asthma or related respiratory condition') AS ref_vec_0,\n\nsimilar_conditions AS (\n SELECT\n c.PATIENT AS PATIENT,\n c.DESCRIPTION AS DESCRIPTION,\n distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM\n conditions c\n ORDER BY distance\n LIMIT 5\n),\n\nencounter_info AS (\n SELECT\n e.PATIENT AS PATIENT,\n e.DATE AS DATE,\n e.DESCRIPTION AS DESCRIPTION\n FROM\n encounters e\n WHERE\n e.DATE > '2023-01-01'\n)\n\nSELECT\n p.first || ' ' || p.last AS patient_name\nFROM\n patients p\nJOIN\n similar_conditions sc ON toString(p.patient) = toString(sc.PATIENT)\nJOIN\n encounter_info ei ON toString(p.patient) = toString(ei.PATIENT)\nWHERE\n p.gender = 'Female'\n AND ei.DESCRIPTION LIKE '%emergency%'\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Who are the names of a few women who have had emergency health visits this year and show signs of respiratory issues like asthma?", + "external_knowledge": "The SQL query applies a vector similarity search using the `sqlite-lembed` extension, which finds approximate nearest neighbors based on semantic similarity. It uses the Euclidean distance (L2 norm) to measure how closely the medical condition descriptions relate to the vector representation of \"Asthma or related respiratory condition.\" The parameter `k=5` specifies that the top 5 most similar patients should be selected. Semantic similarity here implies that conditions semantically close to asthma are prioritized, and the concept of 'a few' in the question refers to selecting up to 5 patients based on this similarity. The query also uses standard SQL operations to filter based on gender and encounter details.", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for general health') AS ref_vec_0\n\nSELECT ID, DATE, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the ID and date of the encounter that most closely resembles a routine check-up for general health?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0\n\nSELECT p.first, distance(pr.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS pr\nJOIN patients AS p ON toString(pr.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey, can you fetch me the names of the top 5 patients who have had procedures similar to a standard pregnancy test? Thanks!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic cough') AS ref_vec_0,\n\nSimilarConditions AS (\n SELECT c.PATIENT, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions AS c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.first, p.last, p.gender, p.race, p.ethnicity, sc.DESCRIPTION\nFROM patients AS p\nJOIN SimilarConditions AS sc ON toString(p.patient) = toString(sc.PATIENT)\nORDER BY sc.distance;", + "sql_result_column_count": 6, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me out by finding the top 5 patients who have conditions most related to a \"Chronic cough\"? I'd like to know their first and last names, gender, race, ethnicity, and a brief description of their conditions. Thanks!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up') AS ref_vec_0,\n\nPatientDetails AS (\n SELECT\n p.patient AS patient,\n p.first AS first,\n p.last AS last,\n p.gender AS gender,\n p.birthdate AS birthdate\n FROM\n patients p\n WHERE\n p.gender = 'Female'\n)\n\nSELECT\n e.DESCRIPTION, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM\n encounters e\nJOIN\n PatientDetails pd ON toString(e.PATIENT) = toString(pd.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Please provide the descriptions of the top 5 encounters that are most aligned with a \"Routine check-up\" for female patients, ensuring the results are ordered by their similarity to the concept.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Amoxicillin 500mg oral capsule') AS ref_vec_0\n\nSELECT m.DESCRIPTION, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications AS m\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 medications that are closely related to \"Amoxicillin 500mg oral capsule\" and provide their descriptions?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0\n\nSELECT p.DESCRIPTION, distance(p.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS p\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Can you uncover the trio of medical procedures that narrate tales akin to the chronic struggles of obstructive pulmonary disease? Share their stories and the closeness of their tales' resonance.", + "external_knowledge": "The `MATCH` operation in vector searches is used to perform approximate nearest neighbor (ANN) search, which identifies items closest to a given vector representation. In this case, the `lembed` function generates a vector for \"Chronic obstructive pulmonary disease,\" and the search retrieves the top 3 procedures most similar to this concept as measured by Euclidean distance. The smaller the distance, the greater the similarity in the vector space. Understanding chronic obstructive pulmonary disease involves recognizing it as a long-term pulmonary condition that causes breathing difficulties, often requiring specific medical procedures for management.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up procedure') AS ref_vec_0\n\nSELECT DATE, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, could you tell me the date of the procedure that most closely matches a routine check-up? I'd like to know how similar it is as well!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic bronchitis condition') AS ref_vec_0\n\nSELECT PATIENT, ENCOUNTER, DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you identify a few cases where patients had conditions described like chronic bronchitis and provide their names and encounter details?", + "external_knowledge": "The `MATCH` operator is employed to perform an approximate nearest neighbor (ANN) search, identifying entries in the database that are semantically similar to a provided description. The `lembed()` function converts textual descriptions into embeddings using the 'all-MiniLM-L6-v2' model, which is commonly used for generating vector representations of text. The query specifies `k=5`, meaning it will return the top 5 records that most closely match the concept of \"Chronic bronchitis condition.\" In vector similarity searches, the similarity between embeddings is determined based on Euclidean distance, where a smaller distance indicates a higher degree of similarity.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chest pain and shortness of breath') AS ref_vec_0\n\nSELECT e.DESCRIPTION, p.first || ' ' || p.last AS patient_name, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.ethnicity = 'Hispanic or Latino'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top three medical encounters related to \"Chest pain and shortness of breath\" for patients of Hispanic or Latino ethnicity, and provide the descriptions of these encounters along with the patients' full names.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis diagnosis') AS ref_vec_0\n\nSELECT PATIENT, DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a handful of cases related to acute bronchitis and tell me who they involve?", + "external_knowledge": "The `MATCH` operator in this query is used for performing an approximate nearest neighbor (ANN) search, which is an efficient way to find items that are closest in meaning or context based on vector embeddings. In this case, the `DESCRIPTION_embedding` column is being compared to a vector generated from the phrase \"Acute bronchitis diagnosis\". The `k = 5` parameter indicates that the query will return the 5 most similar cases, with similarity determined by the Euclidean distance between vector representations—where smaller distances correspond to higher similarity. This approach is widely used in information retrieval and natural language processing to find semantically related items.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Annual Physical Exam') AS ref_vec_0\n\nSELECT ID, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the IDs of the top 5 encounters related to an annual physical exam?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n\nliving_patients AS (\n SELECT patient\n FROM patients\n WHERE deathdate IS NULL\n)\n\nSELECT p.DESCRIPTION, distance(p.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS p\nJOIN living_patients AS lp ON toString(p.PATIENT) = toString(lp.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Unveil the top 5 tales of medical procedures performed on currently breathing souls, where the whispers of \"Acute bronchitis\" linger closely in the air. What are these tales?", + "external_knowledge": "The query uses semantic vector search, which identifies the most contextually similar items to a given phrase, here \"Acute bronchitis (disorder)\". The `MATCH` operator is part of an approximate nearest neighbor (ANN) search, designed to efficiently find vectors (in this case, procedure descriptions) that are closest in meaning to the provided text embedding. The `k=5` parameter limits the results to the top 5 most relevant entries by measuring distances, where a smaller distance indicates a higher similarity. The embedding model 'all-MiniLM-L6-v2' transforms textual data into a numerical format, allowing for such spatial comparisons.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'common cold treatment') AS ref_vec_0\n\nSELECT m.DESCRIPTION, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications AS m\nJOIN patients AS p ON toString(m.PATIENT) = toString(p.patient)\nWHERE p.gender = 'female'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please find the top 3 medication descriptions related to common cold treatment for female patients? Make sure to list them in order of relevance!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Asthma attack condition') AS ref_vec_0\n\nSELECT p.first, p.last, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the top 5 patients who have conditions most related to an asthma attack? I’d love to know their first and last names.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chest pain and difficulty breathing') AS ref_vec_0\n\nSELECT distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.gender = 'female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in finding some medical encounters.\n**Assistant**: What kind of medical encounters are you looking for?\n**User**: Encounters related to symptoms like chest pain and difficulty breathing.\n**Assistant**: How many of these encounters would you like to find?\n**User**: I would like to find the top 5 encounters.\n**Assistant**: Are there any specific patient demographics you are interested in?\n**User**: Yes, I'm specifically interested in encounters involving female patients.\n**Assistant**: Alright, I will help you search for the top 5 medical encounters related to 'chest pain and difficulty breathing' for female patients.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Urinary tract infection') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Regular health examination') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.PATIENT\nFROM c_filtered AS c\nJOIN e_filtered AS e ON toString(c.ENCOUNTER) = toString(e.ID)\nORDER BY c.distance, e.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "In the vast garden of medical records, who are the five patients entangled in the web of urinary tract infections, while their paths also brush against the tranquil breeze of regular health check-ups?", + "external_knowledge": "The `MATCH` operator is used here to find the most similar embeddings to the given phrases. This involves an approximate nearest neighbor search where the \"k = 5\" indicates the top 5 most similar records to be retrieved. The embeddings are compared using Euclidean distance, where a smaller distance suggests higher similarity. These vector searches allow the identification of conditions and encounters that are conceptually similar to \"Urinary tract infection\" and \"Regular health examination,\" respectively.", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function during an encounter') AS ref_vec_0\n\nSELECT DATE, PATIENT, ENCOUNTER, CODE, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I'm interested in learning about specific medical procedures.\"\n**Assistant**: \"Could you specify what type of medical procedures you are curious about?\"\n**User**: \"I'm looking for procedures related to the measurement of respiratory function during an encounter.\"\n**Assistant**: \"How many examples of such procedures would you like to see?\"\n**User**: \"I would like to see the top 5 procedures that best fit this description.\"\n**Assistant**: \"Perfect, I will find the best 5 procedures related to the measurement of respiratory function during encounters for you.\"\n**User**: \"What sort of information will I get about these procedures?\"\n**Assistant**: \"You will receive the date of each procedure, the patient identifier, the encounter code, the procedure code, and how closely each procedure matches your description.\"\n**User**: \"Great, thank you!\"\n**Assistant**: \"You're welcome! Let me translate your request into an SQL query.\"", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Antibiotic used for bacterial infections') AS ref_vec_0\n\nSELECT distance(medications.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, could you find me the distance for the medication that best fits the description of an antibiotic used to treat bacterial infections? Thanks!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute respiratory distress') AS ref_vec_0,\n\nPatientDemographics AS (\n SELECT patient\n FROM patients\n WHERE gender = 'Female' AND race = 'Asian'\n)\n\nSELECT c.PATIENT, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN PatientDemographics p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who are the top 5 Asian female patients experiencing acute respiratory distress based on their medical records?", + "external_knowledge": "The query leverages vector search, specifically approximate nearest neighbor (ANN) search, which identifies items most semantically similar to a given concept—in this case, \"Acute respiratory distress.\" The `MATCH` operator performs this search, using the embeddings from `lembed('all-MiniLM-L6-v2', ...)`, a pre-trained language model. The parameter `k=5` specifies that the search will return the top 5 conditions that are most similar to the input concept. Similarity is calculated using Euclidean distance, where lesser distance indicates higher similarity.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to dairy product') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function (procedure)') AS ref_vec_1,\n\nallergies_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM allergies\n\n ORDER BY distance\n LIMIT 5\n),\n\nprocedures_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarAllergies AS (\n SELECT PATIENT, distance \n FROM allergies_filtered AS allergies\n),\n\nSimilarProcedures AS (\n SELECT PATIENT, distance \n FROM procedures_filtered AS procedures\n),\n\nPatientConditions AS (\n SELECT DISTINCT c.PATIENT, ap.PREVALENCE_RATE\n FROM conditions c\n JOIN SimilarAllergies sa ON toString(c.PATIENT) = toString(sa.PATIENT)\n JOIN SimilarProcedures sp ON toString(c.PATIENT) = toString(sp.PATIENT)\n JOIN all_prevalences ap ON toString(c.DESCRIPTION) = toString(ap.ITEM)\n)\n\nSELECT AVG(PREVALENCE_RATE) AS average_prevalence_rate\nFROM PatientConditions;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you please find the prevalence rates of conditions that are common among patients with the top 5 allergies to dairy and top 5 procedures related to respiratory function? After that, calculate the average prevalence rate for these patients' conditions!", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Cardiac surgery is performed to correct heart defects or conditions') AS ref_vec_0\n\nSELECT DATE, PATIENT, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the dates, patient identifiers, and similarity distances for the top 5 procedures closely related to cardiac surgery aimed at correcting heart defects or conditions?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine health checkup including blood tests') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Hypertension management') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.distance\nFROM p_filtered AS p\nJOIN c_filtered AS c ON toString(p.PATIENT) = toString(c.PATIENT)\nORDER BY p.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you pull up the top 10 procedures for me that are related to routine health checkups with blood tests and are linked to patients managing hypertension? I'd love to see the similarity distances for these.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outpatient Encounter') AS ref_vec_0\n\nSELECT e.ID, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters AS e\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the IDs of the top 5 encounters most relevant to the concept of an \"Outpatient Encounter,\" sorted by their similarity.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Bladder infection') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Urinary tract infection') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 10\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarConditions AS (\n SELECT \n c.PATIENT AS PATIENT,\n c.DESCRIPTION AS DESCRIPTION,\n c.distance as condition_distance\n FROM c_filtered AS c\n)\n\nSELECT \n p.first || ' ' || p.last AS full_name,\n e.DESCRIPTION AS DESCRIPTION,\n e.distance as encounter_distance\nFROM \n SimilarConditions sc\nJOIN \n patients p ON toString(sc.PATIENT) = toString(p.patient)\nJOIN e_filtered AS e ON toString(e.PATIENT) = toString(p.patient)\nORDER BY \n sc.condition_distance, e.distance;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Who are the patients with some of the closest conditions to a bladder infection, and what are the few encounters they had that are like a urinary tract infection?", + "external_knowledge": "The query utilizes vector search operations through the `lembed` function, which compares text embeddings to find semantically similar items. The \"MATCH\" operator performs an approximate nearest neighbor (ANN) search to retrieve items that are closest in meaning based on their vector representations. The parameter `k=N` specifies how many of these similar items should be retrieved. In this context, semantic embeddings are used to identify patients with conditions and encounters similar to specific medical conditions. The similarity is determined by the Euclidean distance between vector representations, with smaller distances indicating higher similarity.", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Kidney Disease') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Penicillin Allergy') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM allergies\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelevantConditions AS (\n SELECT \n PATIENT, \n DESCRIPTION AS ConditionDescription, \n c.distance AS distance\n FROM c_filtered AS c\n)\n\nSELECT \n rc.PATIENT AS PATIENT, \n a.DESCRIPTION AS AllergyDescription\nFROM \n RelevantConditions rc\nJOIN a_filtered AS a ON toString(rc.PATIENT) = toString(a.PATIENT)\nORDER BY \n rc.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Find patients with conditions closely related to Chronic Kidney Disease and allergies closely related to Penicillin Allergy. List their patient IDs and allergy descriptions, ordered by condition relevance.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Patient diagnosed with acute bronchitis and requires follow-up') AS ref_vec_0\n\nSELECT ID, DESCRIPTION, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you help me find the top 5 patient encounters that are most similar to a case where someone was diagnosed with acute bronchitis and needs a follow-up? I'd love to know their IDs and descriptions.", + "external_knowledge": "", + "integration_level": 2, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Antibiotic for bacterial infection') AS ref_vec_0\n\nSELECT m.DESCRIPTION, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications m\nJOIN patients p ON toString(m.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Return the descriptions of the top 3 medications that are most relevant to the use of antibiotics for bacterial infections.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Routine checkup') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 3\n),\n\nPatientConditions AS (\n SELECT DISTINCT c.PATIENT\n FROM conditions c\n WHERE c.DESCRIPTION = 'Cystitis'\n),\n\nMedicationEncounters AS (\n SELECT DISTINCT m.PATIENT, m.ENCOUNTER\n FROM medications m\n INNER JOIN PatientConditions pc ON toString(pc.PATIENT) = toString(m.PATIENT)\n WHERE m.DESCRIPTION = 'Penicillin V Potassium 250 MG'\n),\n\nSimilarEncounters AS (\n SELECT e.ID, e.PATIENT, e.DESCRIPTION, e.distance\n FROM e_filtered AS e\n INNER JOIN MedicationEncounters me ON toString(me.ENCOUNTER) = toString(e.ID)\n),\n\nPatientProcedures AS (\n SELECT p.DATE, p.PATIENT, p.DESCRIPTION, p.distance\n FROM p_filtered AS p\n INNER JOIN SimilarEncounters se ON toString(se.PATIENT) = toString(p.PATIENT)\n)\n\nSELECT sp.DESCRIPTION\nFROM PatientProcedures sp\nORDER BY sp.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the realm of healing, where clouds of ailments and cures drift endlessly, identify the solitary procedure akin to a \"Routine checkup\" for patients who battle \"Cystitis\" and have been touched by the healing essence of \"Penicillin V Potassium 250 MG,\" while their encounters echo the whispers of \"Acute bronchitis.\"", + "external_knowledge": "The query utilizes advanced vector operations to perform semantic searches within textual data. The `MATCH lembed` function uses a model named \"all-MiniLM-L6-v2\" to calculate embeddings, which are multi-dimensional representations of the text data. These embeddings allow the system to measure semantic similarity instead of just textual match, significantly enhancing the search to capture context and meaning. The \"k = 3\" indicates the retrieval of the top three most similar items based on Euclidean distance, where a smaller distance indicates higher similarity. This approach enables the database to find records that resonate in theme and context with specified medical conditions and procedures, even if the text does not match exactly.", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine medical checkup for healthy individuals') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Annual flu shot administration') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM immunizations\n\n ORDER BY distance\n LIMIT 5\n),\n\nRecentEncounters AS (\n SELECT e.ID, e.PATIENT, e.DESCRIPTION, e.distance\n FROM e_filtered AS e\n ORDER BY e.distance\n),\n\nPatientImmunizations AS (\n SELECT i.DATE, i.PATIENT, i.DESCRIPTION, i.distance\n FROM i_filtered AS i\n ORDER BY i.distance\n)\n\nSELECT re.ID AS Encounter_ID, pi.PATIENT AS Patient_ID, pi.DESCRIPTION AS Immunization_Description\nFROM RecentEncounters re\nJOIN PatientImmunizations pi ON toString(re.PATIENT) = toString(pi.PATIENT)\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the Encounter IDs, Patient IDs, and Immunization Descriptions for 10 patients who had recent routine medical checkups and also received their annual flu shots? I need this information urgently!", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine health check-up with focus on preventive care') AS ref_vec_0\n\nSELECT ID, DESCRIPTION, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the IDs and descriptions for the top 5 encounters that closely align with routine health check-ups focused on preventive care?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Cystitis') AS ref_vec_0\n\nSELECT p.patient, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Who are the 5 patients with conditions most relevant to cystitis and what are their similarity distances?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0\n\nSELECT p.first, distance(pr.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM patients p\nJOIN procedures pr ON toString(p.patient) = toString(pr.PATIENT)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the first names of the 5 patients who have undergone procedures similar to a standard pregnancy test?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Obstructive Pulmonary Disease') AS ref_vec_0\n\nSELECT c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN encounters AS e ON toString(c.ENCOUNTER) = toString(e.ID)\nWHERE e.DATE > '2023-01-01'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to find the top 5 descriptions of medical conditions that are most similar to \"Chronic Obstructive Pulmonary Disease\" in terms of semantic meaning, from recent encounters occurring after January 1, 2023. Can you provide these descriptions ordered by their relevance?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Peanut allergy reaction') AS ref_vec_0\n\nSELECT START, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "In the realm of peanut-induced turmoil, what singular moment emerges as the most vivid tale of allergic woe?", + "external_knowledge": "For this query, the `MATCH` operator orchestrates an approximate nearest neighbor (ANN) search, a dance of data where vectors are compared using the Euclidean distance. This mathematical choreography seeks to find the closest match to the idea of a \"Peanut allergy reaction,\" as embedded in the database. The parameter `k = 1` signals the search to unveil only one instance—the closest kin in this conceptual family. By default, smaller distances in this space indicate a stronger resemblance or similarity.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0\n\nSELECT p.DESCRIPTION, c.DESCRIPTION, distance(p.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS p\nJOIN conditions AS c ON toString(p.ENCOUNTER) = toString(c.ENCOUNTER)\nWHERE c.PATIENT = 'example_patient_id'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Amid the tapestry of medical history, unveil the top 5 tales of procedures that echo the essence of a \"Standard pregnancy test\" for our protagonist, known as 'example_patient_id', accompanied by the whispers of conditions entwined with each act.", + "external_knowledge": "In vector-based searches like the one used here, the `MATCH` operator performs an approximate nearest neighbor (ANN) search to identify items that are most similar to a given concept—in this case, \"Standard pregnancy test\". The `k=5` clause specifies that the top 5 most similar procedures should be returned. Vector comparisons are generally done using the Euclidean distance (L2 norm), where a smaller distance indicates greater similarity. The 'all-MiniLM-L6-v2' model is used to generate embeddings that encapsulate the semantic meaning of terms, enabling the search for procedures that align closely with the concept being queried.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Normal gestation') AS ref_vec_0\n\nSELECT distance(p.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures p\nJOIN patients pa ON toString(p.PATIENT) = toString(pa.patient)\nWHERE pa.gender = 'female'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the distance metrics for the top 3 procedures related to \"Normal gestation\" performed on female patients?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Peanut allergy') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM allergies\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you help me find the top 3 allergy descriptions that are closely related to a peanut allergy?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for annual health assessment') AS ref_vec_0,\n\nSimilarEncounters AS (\n SELECT e.ID, e.PATIENT, e.REASONDESCRIPTION, distance(e.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters e\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT se.ID, p.first || ' ' || p.last AS patient_name, p.gender\nFROM SimilarEncounters se\nJOIN patients p ON toString(se.PATIENT) = toString(p.patient)\nORDER BY se.distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Could you tell me who those select patients are that just had routine health check-ups? I need their names, genders, and encounter IDs.", + "external_knowledge": "The `MATCH` operator in this context performs an approximate nearest neighbor (ANN) search to find encounters with reason descriptions most similar to \"Routine check-up for annual health assessment\". The vector similarity is determined using embedded vectors, typically comparing them based on Euclidean distance (L2 norm). Specifying `k=5` indicates we are interested in the top 5 nearest neighbors based on this similarity measure, while the `LIMIT 3` clause in the main query restricts the output to the top 3 most similar encounters. The lower the distance, the higher the similarity between the encounters' reason descriptions and the target concept.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic kidney condition with frequent urinary tract infections') AS ref_vec_0\n\nSELECT c.DESCRIPTION, p.first, p.last, p.gender, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "Can you identify the five individuals who are battling the storm of chronic kidney complications coupled with frequent urinary infections, and tell me their names, along with the pathways of their gender?", + "external_knowledge": "The `MATCH` operator in SQLite is used to perform an approximate nearest neighbor (ANN) search, which identifies items that are closest in vector space to a provided embedding. The `lembed` function applies a specific model (`all-MiniLM-L6-v2`) to generate embeddings for semantic comparison. In this context, similarity is measured using Euclidean distance, meaning the lower the distance, the higher the similarity. The `k=5` parameter specifies that the query returns the top 5 closest matches, indicating the most representative conditions that align with \"Chronic kidney condition with frequent urinary tract infections.\"", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_0\n\nSELECT p.first, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Who is the patient associated with the top care plan related to respiratory therapy?", + "external_knowledge": "The `MATCH` operation in the query uses an approximate nearest neighbor (ANN) search, which helps in finding vectors (here, care plan descriptions) that are closest to the given vector representation of \"Respiratory therapy\". The `lembed` function generates these embeddings using the 'all-MiniLM-L6-v2' language model. The parameter `k=1` ensures that only the single most relevant care plan is selected, reflecting the highest similarity to the query vector. Generally, in vector searches, similarity is determined by the proximity of vector positions, typically using measures like Euclidean distance, where a smaller distance indicates a higher similarity.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to dust mites') AS ref_vec_0\n\nSELECT distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM allergies\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'd like to get some information on allergies.\n**Assistant**: What specific allergy are you interested in?\n**User**: I'm looking for information related to an allergy to dust mites.\n**Assistant**: How many similar allergies would you like to find?\n**User**: About 5 should be enough.\n**Assistant**: Alright, I'll find the top 5 allergies that are most relevant to an allergy to dust mites and provide their similarity distances.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for fever') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the descriptions of the top 5 medical encounters that are related to a routine check-up for fever?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n\nEncounterMatches AS (\n SELECT ID, PATIENT, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT p.first, p.last, p.birthdate, p.gender, em.distance\nFROM EncounterMatches em\nJOIN patients p ON toString(em.PATIENT) = toString(p.patient)\nORDER BY em.distance;", + "sql_result_column_count": 5, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "List the first and last names, birthdates, and genders of patients involved in the top 10 encounters related to acute bronchitis, ordered by similarity.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0\n\nSELECT distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN all_prevalences ap ON toString(ap.ITEM) = toString(c.DESCRIPTION)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the realm where ailments whisper their tales, which condition stands closest at the doorstep of \"Chronic obstructive pulmonary disease,\" as judged by the smallest leap of distance in understanding?", + "external_knowledge": "In vector search operations within databases like SQLite with the `sqlite-vec` extension, the `MATCH` keyword is used to perform an approximate nearest neighbor (ANN) search. This is designed to find entries within a database that are most similar to a given input vector, based on learned embeddings. The `lembed()` function generates a vector representation of the input phrase, and the search returns the top `k` results that are most similar to this vector. The `distance` column measures how similar each result is to the input; a smaller distance indicates a higher similarity. Thus, in the context of diseases or conditions, a smaller distance suggests a condition that shares closer characteristics or semantics with the specified disease.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Antibiotic medication for bacterial infection') AS ref_vec_0\n\nSELECT m.DESCRIPTION, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications m\nJOIN patients p ON toString(m.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "In the vast garden of pharmaceuticals, which are the five medications that bloom best when seeking remedies akin to antibiotic solutions for bacterial infections?", + "external_knowledge": "The `MATCH` operator in this context is performing an approximate nearest neighbor (ANN) search, which is a technique used to find items in a dataset that are most similar to a given input. Here, we are using the `lembed` function with the model 'all-MiniLM-L6-v2' to create vector embeddings of medication descriptions. The query is configured to return the top 5 medications (`m.k = 5`) that closely align with the concept of \"Antibiotic medication for bacterial infection.\" The results are sorted by their Euclidean distance, with smaller distances indicating higher similarity.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common cold (disorder)') AS ref_vec_0\n\nSELECT ITEM, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance \nFROM all_prevalences\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "What is the item from the prevalence list that is most closely associated with the common cold disorder, based on the vector similarity search, and what is its similarity distance?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common cold (disorder)') AS ref_vec_0\n\nSELECT e.ID, p.first || ' ' || p.last AS full_name, distance(e.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you provide the IDs and full names of the top 3 patients involved in encounters that are most associated with the common cold disorder, sorted by their relevance?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine health check procedure') AS ref_vec_0\n\nSELECT distance(p.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures p\nJOIN encounters e ON toString(p.ENCOUNTER) = toString(e.ID)\nWHERE e.REASONDESCRIPTION LIKE '%check%'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Find the top 3 procedures related to a routine health check, ensuring their encounter reasons include \"check\". List their distances.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to peanuts') AS ref_vec_0\n\nSELECT ITEM, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance \nFROM all_prevalences\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the item most closely related to \"Allergy to peanuts\" from the `all_prevalences` table? I just need the name of that item!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for respiratory issues') AS ref_vec_0,\n\nEncounterCTE AS (\n SELECT\n e.ID AS ID,\n e.DATE AS DATE,\n e.PATIENT AS PATIENT,\n e.DESCRIPTION AS DESCRIPTION,\n distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM\n encounters e\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT\n ec.ID AS EncounterID,\n p.first || ' ' || p.last AS PatientName,\n ec.DESCRIPTION AS EncounterDescription\nFROM\n EncounterCTE ec\nJOIN\n patients p ON toString(ec.PATIENT) = toString(p.patient)\nORDER BY\n ec.distance AS distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the top 5 encounters involving patients that are most related to \"Routine check-up for respiratory issues,\" including the encounter IDs, patient names, and descriptions?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Encounter for chest pain') AS ref_vec_0\n\nSELECT e.ID, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the IDs of the top 5 encounters related to chest pain for female patients? I'm curious to know which ones are the most similar!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic respiratory conditions') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_1,\n\nconditions_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\nprocedures_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 5\n),\n\nConditionMatches AS (\n SELECT PATIENT, distance AS condition_distance\n FROM conditions_filtered AS conditions\n),\n\nProcedureMatches AS (\n SELECT PATIENT, distance AS procedure_distance\n FROM procedures_filtered AS procedures\n)\n\nSELECT c.PATIENT\nFROM ConditionMatches c\nJOIN ProcedureMatches p ON toString(c.PATIENT) = toString(p.PATIENT)\nORDER BY c.condition_distance + p.procedure_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the patient who most closely matches both chronic respiratory conditions and respiratory therapy, based on the top 5 vector embeddings for each condition, and provide the one with the highest combined relevance.", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up with mild symptoms and preventive measures') AS ref_vec_0\n\nSELECT e.ID, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters AS e\nJOIN patients AS p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you track down the single patient encounter ID that's most closely associated with a routine check-up with some mild symptoms and preventive measures?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic sinusitis') AS ref_vec_0\n\nSELECT distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM patients p\nJOIN conditions c ON toString(p.patient) = toString(c.PATIENT)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the similarity distances for the top 3 conditions most related to \"Chronic sinusitis\" among the patients?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'NuvaRing 0.12/0.015 MG per 24HR 21 Day Vaginal Ring') AS ref_vec_0\n\nSELECT m.DESCRIPTION, p.first, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications AS m\nJOIN patients AS p ON toString(m.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I'm interested in finding details about some medications.\"\n**Assistant**: \"Sure, could you tell me what specific medication you're interested in?\"\n**User**: \"I'm looking for medications similar to NuvaRing.\"\n**Assistant**: \"Alright. How many similar medications would you like to find?\"\n**User**: \"I'd like to find the top 5 similar medications.\"\n**Assistant**: \"Got it. Besides the medication details, what other information would you like to know?\"\n**User**: \"I want to know the names of the patients using these medications.\"\n**Assistant**: \"Is there anything else you'd like to include?\"\n**User**: \"No, that's all.\"\n**Assistant**: \"Okay, I'll help you find the top 5 medications similar to NuvaRing and include the first names of associated patients.\"", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function procedure') AS ref_vec_0\n\nSELECT p.ethnicity, distance(pr.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS pr\nJOIN patients AS p ON toString(pr.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the ethnicities of patients who have had the top 5 procedures associated with measuring respiratory function?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Antibiotic medication') AS ref_vec_0\n\nSELECT p.first || ' ' || p.last AS full_name, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications m\nJOIN patients p ON toString(m.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please find the top 5 female patients who have been prescribed medications closely related to antibiotics and provide their full names?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outpatient visit for respiratory issues') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Respiratory disorder') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelevantEncounters AS (\n SELECT e.ID, e.DATE, e.PATIENT, e.DESCRIPTION, e.distance\n FROM e_filtered AS e\n),\n\nRelevantConditions AS (\n SELECT c.START, c.STOP, c.PATIENT, c.DESCRIPTION, c.distance\n FROM c_filtered AS c\n)\n\nSELECT p.first, p.last, p.birthdate\nFROM patients p\nJOIN RelevantEncounters re ON toString(p.patient) = toString(re.PATIENT)\nJOIN RelevantConditions rc ON toString(p.patient) = toString(rc.PATIENT)\nORDER BY re.distance, rc.distance\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "List the names and birthdates of the top 10 patients with both encounters and conditions related to respiratory issues.", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine Checkup') AS ref_vec_0,\n\nEncounterAnalysis AS (\n SELECT \n e.ID AS encounter_id, \n e.DATE AS encounter_date,\n e.PATIENT AS patient_id, \n e.DESCRIPTION AS encounter_description,\n e.REASONDESCRIPTION AS reason_description,\n distance(e.DESCRIPTION_embedding, ref_vec_0) AS description_distance\n FROM \n encounters e\n ORDER BY description_distance\n LIMIT 10\n),\n\nPatientInfo AS (\n SELECT \n p.patient AS patient_id,\n p.first AS first_name,\n p.last AS last_name,\n p.race AS race,\n p.ethnicity AS ethnicity,\n p.gender AS gender,\n p.birthdate AS birth_date\n FROM \n patients p\n),\n\nEncounterDetails AS (\n SELECT \n ea.encounter_id AS encounter_id,\n pi.first_name || ' ' || pi.last_name AS full_name,\n pi.birth_date AS birth_date,\n pi.race AS race,\n pi.ethnicity AS ethnicity,\n pi.gender AS gender,\n ea.encounter_date AS encounter_date,\n ea.encounter_description AS encounter_description,\n ea.reason_description AS reason_description,\n ea.description_distance AS description_distance\n FROM \n EncounterAnalysis ea\n JOIN \n PatientInfo pi ON toString(ea.patient_id) = toString(pi.patient_id)\n)\n\nSELECT \n ed.encounter_id AS encounter_id\nFROM \n EncounterDetails ed\nORDER BY \n ed.description_distance AS description_distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you provide the IDs of the top 5 encounters most associated with \"Routine Checkup\" based on similarity, and include the related patient demographics?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Viral Sinusitis (Disorder)') AS ref_vec_0\n\nSELECT ap.ITEM, p.first, p.last, distance(ap.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences AS ap\nJOIN patients AS p ON toString(ap.POPULATION_TYPE) = toString(p.gender)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Can you find the top 5 cases of \"Viral Sinusitis (Disorder)\" and share the item names along with the first and last names of the patients? Please make sure they're listed by how closely they relate to the condition!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Physical therapy session for injury recovery') AS ref_vec_0\n\nSELECT e.ID, p.first, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Could you unveil the chronicles of the top 5 healing journeys where patients tread the path of recovery through the art of physical therapy? Reveal their identifiers and the proximity of their tales.", + "external_knowledge": "Vector operations in this query employ the `MATCH` operator to execute a semantic search using embedding vectors. The `lembed()` function with 'all-MiniLM-L6-v2' leverages pre-trained language models to measure the semantic similarity of text descriptions. The query identifies the top 5 closest encounters related to \"Physical therapy session for injury recovery\" by comparing their embedding vectors and ranks them by Euclidean distance; shorter distances suggest closer semantic relevance. This method is particularly effective for capturing nuanced meanings beyond exact keyword matches.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0,\n\nConditionPatients AS (\n SELECT PATIENT, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT p.first\nFROM patients p\nJOIN ConditionPatients cp ON toString(p.patient) = toString(cp.PATIENT)\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Who are some of those patients dealing with breathing issues?", + "external_knowledge": "The `MATCH` operator in the query is used to perform an approximate nearest neighbor (ANN) search, which identifies items most similar to a given query vector. The parameter `k=10` specifies that the search should return the top 10 most similar items. Embeddings are vector representations that capture semantic meaning and are compared using Euclidean distance (L2 norm) by default, with similarity increasing as distance decreases. In domain-specific terms, \"Chronic obstructive pulmonary disease\" refers to a group of lung conditions that cause breathing difficulties, often requiring inference from vectorized condition descriptions.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Flu vaccination procedure') AS ref_vec_0\n\nSELECT p.patient, distance(proc.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS proc\nJOIN patients AS p ON toString(proc.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Who are the 5 patients that have undergone a procedure related to a flu vaccination?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Physical therapy recommended for knee recovery') AS ref_vec_0\n\nSELECT c.ID, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nWHERE p.gender = 'female'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the IDs of the top 3 care plans recommended for physical therapy related to knee recovery, specifically for female patients?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n\nPatientConditions AS (\n SELECT c.PATIENT, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions c\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT p.first, p.last\nFROM patients p\nJOIN PatientConditions pc ON toString(p.patient) = toString(pc.PATIENT)\nORDER BY pc.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I need some information about patients in our database.\"\n**Assistant**: \"What specific condition-related information are you interested in?\"\n**User**: \"I'm particularly looking for patients with conditions related to Acute bronchitis.\"\n**Assistant**: \"How many patients would you like to find, and how should their conditions be related to Acute bronchitis?\"\n**User**: \"I'd like to identify 10 patients whose conditions are most similar to Acute bronchitis.\"\n**Assistant**: \"What details do you need about these patients?\"\n**User**: \"I want to know their first and last names.\"\n**Assistant**: \"Is there anything else you would like to include or sort by in this information?\"\n**User**: \"No, that's all. Just sort them by how closely their conditions match Acute bronchitis.\"\n**Assistant**: \"Great, I will help you translate your request into an SQL query.\"", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Advice to limit physical activity') AS ref_vec_0,\n\nCarePlanExerciseCTE AS (\n SELECT \n cp.ID AS CarePlanID,\n cp.PATIENT AS PatientID,\n cp.DESCRIPTION AS DESCRIPTION,\n distance(cp.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM careplans cp\n ORDER BY distance\n LIMIT 5\n),\n\nPatientInfoCTE AS (\n SELECT \n p.patient AS PatientID,\n p.first AS FirstName,\n p.last AS LastName,\n p.birthdate AS BirthDate\n FROM patients p\n)\n\nSELECT \n pi.FirstName AS FirstName,\n pi.LastName AS LastName\nFROM CarePlanExerciseCTE cpe\nJOIN PatientInfoCTE pi ON toString(cpe.PatientID) = toString(pi.PatientID)\nORDER BY cpe.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Who are the top 10 patients with care plans advising to limit physical activity? List their first and last names, ordered by similarity.", + "external_knowledge": "", + "integration_level": 2, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic headache condition') AS ref_vec_0\n\nSELECT c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the top 3 conditions related to chronic headaches.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Sore throat') AS ref_vec_0\n\nSELECT c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the top 3 descriptions of health conditions that are closely associated with a sore throat?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine health checkup') AS ref_vec_0\n\nSELECT \n e.DATE AS DATE, \n p.first || ' ' || p.last AS patient_name,\n e.DESCRIPTION AS DESCRIPTION,\n distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM \n encounters e\nJOIN \n patients p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm looking to find details about some patient encounters.\n**Assistant**: What type of encounters are you interested in?\n**User**: I'm specifically interested in routine health checkups.\n**Assistant**: How many encounters would you like to retrieve?\n**User**: I'd like to see the top 3 encounters.\n**Assistant**: Do you want to know about the patients involved as well?\n**User**: Yes, I'd like to know their names along with the encounter details.\n**Assistant**: Is there anything else you need in the results, like sorting parameters?\n**User**: I'd like them sorted by relevance to my query.\n**Assistant**: Alright, I will help you convert your request into an SQL query that retrieves the top 3 most relevant routine health checkup encounters, along with patient names, sorted by relevance.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Encounter for respiratory symptoms') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Viral Sinusitis (Disorder)') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantEncounters AS (\n SELECT e.ID, e.PATIENT, e.distance\n FROM e_filtered AS e\n ORDER BY e.distance\n),\n\nPatientConditions AS (\n SELECT c.PATIENT, c.DESCRIPTION\n FROM c_filtered AS c\n JOIN RelevantEncounters re ON toString(c.PATIENT) = toString(re.PATIENT)\n)\n\nSELECT pc.PATIENT, ap.PREVALENCE_PERCENTAGE\nFROM PatientConditions pc\nJOIN all_prevalences ap ON toString(ap.ITEM) = toString(pc.DESCRIPTION)\nORDER BY ap.PREVALENCE_PERCENTAGE DESC\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 10 patients who had significant encounters for respiratory symptoms and also have a condition like viral sinusitis? I'd love to know how prevalent these conditions are for them!", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Appendectomy is a surgical procedure') AS ref_vec_0\n\nSELECT p.patient, distance(pr.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS pr\nJOIN patients AS p ON toString(pr.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Seek out the top 5 women who have danced with the surgical knife in a manner akin to an appendectomy.", + "external_knowledge": "In vector databases like sqlite-vec, the `MATCH` operation performs an approximate nearest neighbor search to find data points closely related to a given concept—in this case, \"Appendectomy is a surgical procedure\". The `lembed()` function generates vector embeddings using the 'all-MiniLM-L6-v2' model, and the query finds the top 5 elements that are most similar based on Euclidean distance, where lower distance implies higher similarity. The use of `k=5` specifies that the query should return the 5 most similar results.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic bronchitis') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Albuterol') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 10\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM medications\n\n ORDER BY distance\n LIMIT 5\n),\n\nsimilar_conditions AS (\n SELECT c.PATIENT, c.CODE, c.START, c.STOP, c.DESCRIPTION, c.distance\n FROM c_filtered AS c\n),\n\nsimilar_medications AS (\n SELECT m.PATIENT, m.CODE, m.START, m.STOP, m.DESCRIPTION, m.REASONDESCRIPTION, m.distance\n FROM m_filtered AS m\n),\n\nconditions_medications AS (\n SELECT sc.PATIENT, sc.CODE AS condition_code, sc.DESCRIPTION AS condition_description,\n sm.CODE AS medication_code, sm.DESCRIPTION AS medication_description,\n sm.REASONDESCRIPTION AS REASONDESCRIPTION\n FROM similar_conditions AS sc\n JOIN similar_medications AS sm ON toString(sc.PATIENT) = toString(sm.PATIENT)\n),\n\nprevalence_analysis AS (\n SELECT cm.condition_code, cm.condition_description,\n cm.medication_code, cm.medication_description,\n ap.PREVALENCE_PERCENTAGE AS PREVALENCE_PERCENTAGE\n FROM conditions_medications AS cm\n LEFT JOIN all_prevalences AS ap ON toString(cm.condition_code) = toString(ap.ITEM)\n WHERE ap.POPULATION_TYPE = 'GENERAL'\n)\n\nSELECT condition_code\nFROM prevalence_analysis\nORDER BY prevalence_analysis.PREVALENCE_PERCENTAGE DESC\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you tell me which condition code is the most common among patients who have conditions like \"Chronic bronchitis\" and are on medications similar to \"Albuterol\"? I'm curious about the one with the highest prevalence in the general population!", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outpatient Encounter') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Allergy to dust') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM allergies\n\n ORDER BY distance\n LIMIT 5\n),\n\nOutpatientEncounters AS (\n SELECT e.ID AS encounter_id, e.PATIENT AS patient_id, e.DESCRIPTION AS encounter_description, e.distance AS encounter_distance\n FROM e_filtered AS e\n),\n\nPatientAllergies AS (\n SELECT a.PATIENT AS patient_id, a.DESCRIPTION AS allergy_description, a.distance AS allergy_distance\n FROM a_filtered AS a\n)\n\nSELECT oe.patient_id, oe.encounter_id, oe.encounter_description, pa.allergy_description, oe.encounter_distance\nFROM OutpatientEncounters oe\nJOIN PatientAllergies pa ON toString(oe.patient_id) = toString(pa.patient_id)\nORDER BY oe.encounter_distance;", + "sql_result_column_count": 5, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I need some information about patient medical visits.\"\n**Assistant**: \"What type of medical visits are you interested in?\"\n**User**: \"I'm looking for outpatient encounters specifically.\"\n**Assistant**: \"Alright, and how would you like to narrow down these encounters?\"\n**User**: \"I want encounters that are most relevant to the concept of outpatient care.\"\n**Assistant**: \"Understood. How many of these encounters would you like to retrieve?\"\n**User**: \"Just the top 5 closest matches will do.\"\n**Assistant**: \"Is there any other information you need about these patients?\"\n**User**: \"Yes, I also need to know if they have any specific allergies.\"\n**Assistant**: \"Which allergy are you concerned about?\"\n**User**: \"I want to know if they have an allergy to dust.\"\n**Assistant**: \"Okay, I'll help you find the top 5 patients with outpatient encounters and also check if they have an allergy to dust. I'll also order these encounters based on their relevance to outpatient care.\"\n**User**: \"That sounds perfect. Thank you!\"\n**Assistant**: \"You're welcome! Let's generate the SQL query to fulfill your request.\"", + "external_knowledge": "", + "integration_level": 7, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic respiratory disease') AS ref_vec_0\n\nSELECT p.first, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the first names of the top 5 patients whose medical conditions are most relevant to chronic respiratory disease?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Normal pregnancy') AS ref_vec_0\n\nSELECT p.first || ' ' || p.last AS full_name, distance(pr.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS pr\nJOIN patients AS p ON toString(pr.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Who are the top 5 patients associated with the tale of a normal pregnancy, and how far are their experiences from the central theme?", + "external_knowledge": "In the context of this query, the 'MATCH' operator is used to perform an approximate nearest neighbor (ANN) search, which identifies items in a dataset that are closest to a specified vector—in this case, the concept of 'Normal pregnancy'. The 'lembed' function generates embeddings (vector representations) using a predefined model ('all-MiniLM-L6-v2') to capture semantic meanings. The parameter 'k = 5' indicates that the query should return the top 5 closest matches. The Euclidean distance (L2 norm) is used to measure similarity, with smaller distances indicating higher similarity.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'allergy to dust mites') AS ref_vec_0\n\nSELECT PATIENT, DESCRIPTION, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM allergies\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in finding some patients based on their allergies.\n**Assistant**: Sure, what kind of allergy information are you looking for specifically?\n**User**: I'm curious about allergies related to dust mites.\n**Assistant**: How many patients would you like to know about who have these allergies?\n**User**: Five would be great.\n**Assistant**: Alright, I'll help find the top 5 patients with allergies most similar to dust mite allergies. Is there anything else you need?\n**User**: No, that's all for now.\n**Assistant**: Okay, I will help you translate your request into an SQL query.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common allergy description') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a handful of allergy descriptions that seem like common ones?", + "external_knowledge": "In this context, the `MATCH` operator facilitates an approximate nearest neighbor search, optimizing for speed rather than perfect accuracy. The `k = 5` parameter limits the search results to the top 5 most relevant entries. The Euclidean distance (L2 norm) is the default metric used to gauge similarity, with closer distances indicating stronger similarity. The model 'all-MiniLM-L6-v2' provides semantic embeddings, allowing the database to match texts based on underlying meanings rather than exact terms. The phrase \"a handful\" is used metaphorically to imply a small, but significant selection—in this case, the top 5 matches.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for blood pressure') AS ref_vec_0\n\nSELECT e.ID, e.DATE, p.gender, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I'm interested in finding some medical records.\"\n**Assistant**: \"What kind of medical records are you looking for?\"\n**User**: \"I'm looking for routine check-ups related to blood pressure.\"\n**Assistant**: \"Got it. How many such records would you like to retrieve?\"\n**User**: \"Five would be perfect.\"\n**Assistant**: \"Alright. Are there any specific patient criteria you're interested in?\"\n**User**: \"Yes, I am specifically looking for records of female patients.\"\n**Assistant**: \"Understood. I'll search for the top 5 routine check-up encounters for blood pressure involving female patients. Is there anything else you need?\"\n**User**: \"No, that's all.\"\n**Assistant**: \"OK, I'll help you get the encounter IDs, dates, and the patients' gender for those records.\"", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy recommendation') AS ref_vec_0\n\nSELECT ID, distance(careplans.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM careplans\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Find the top 5 care plans that are related to respiratory therapy recommendations and provide their IDs.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease with acute exacerbation') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a description related to a serious lung condition that frequently worsens, and let me know what it says?", + "external_knowledge": "The \"MATCH\" operator in the query is used for performing an approximate nearest neighbor (ANN) search, which identifies items that are close in meaning, rather than an exact match, using vector representations. The vector model 'all-MiniLM-L6-v2' converts textual data into a multi-dimensional vector space, allowing for comparisons based on Euclidean distance. The closer two vector representations are, the more semantically similar they are considered. The \"LIMIT 1\" clause returns the single most similar description to the provided phrase. In this context, the phrase \"Chronic obstructive pulmonary disease with acute exacerbation\" describes a serious lung condition, where \"acute exacerbation\" refers to a sudden worsening of symptoms.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common cold prevalence') AS ref_vec_0\n\nSELECT ITEM, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a handful of items that seem closely related to the prevalence of the common cold?", + "external_knowledge": "In vector operations, the `MATCH` operator is used to find items that are most similar to a given vector representation, often determined by the Euclidean distance between vectors. The `k=5` parameter specifies that the query should return the top 5 items with embeddings most closely matching the concept of \"Common cold prevalence\". The use of vector embeddings allows for a semantic search rather than a simple keyword match, providing results based on contextual similarity.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Symptom: headache and dizziness') AS ref_vec_0,\n\nRelevantPatients AS (\n SELECT patient, first, last\n FROM patients\n WHERE gender = 'Female'\n)\n\nSELECT rp.first || ' ' || rp.last AS patient_name, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM RelevantPatients rp\nJOIN encounters e ON toString(rp.patient) = toString(e.PATIENT)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Find the five female patients who have taken a journey through the valley of headaches and dizziness, and reveal their names alongside the closeness of their encounters.", + "external_knowledge": "The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which identifies items most similar to a given query vector. The `lembed` function generates a semantic embedding based on the text \"Symptom: headache and dizziness\". The parameter `k=5` specifies that the query returns the top 5 most relevant encounters. The Euclidean distance (L2 norm) is used to measure similarity, where a smaller distance indicates a closer match to the query vector. This operation is helpful in identifying encounters that semantically match the concept of specific symptoms, rather than relying on exact keyword matches.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine checkup for a healthy patient') AS ref_vec_0\n\nSELECT ID, DESCRIPTION, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the IDs and descriptions of the top 5 encounters related to a routine checkup for a healthy patient?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in finding some related medical conditions.\n**Assistant**: Which specific medical condition are you interested in exploring?\n**User**: I'm looking into Chronic obstructive pulmonary disease.\n**Assistant**: How many related conditions would you like to find?\n**User**: I'd like to explore about 3 conditions.\n**Assistant**: Great choice. I'll find the top 3 descriptions of conditions that are most relevant to Chronic obstructive pulmonary disease.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Long term management plan for chronic condition') AS ref_vec_0\n\nSELECT p.first, p.last, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans AS c\nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Can you unearth the names of three patients whose long-term care plans echo a symphony of management for chronic conditions, draped in their closest semblance?", + "external_knowledge": "In vector search operations:\n- The \"MATCH\" operator conducts an approximate nearest neighbor (ANN) search to identify the closest matches, based on vector embeddings.\n- The phrase `lembed('all-MiniLM-L6-v2', ...)` specifies the embedding model and the target description, allowing the database to calculate semantic similarity.\n- The parameter `k = 3` indicates that the search is limited to the top 3 most similar items.\n- The results are ranked by their distance in the vector space, meaning that smaller distances imply higher similarity.\n- \"Long term management plan for chronic condition\" is the conceptual target, described in natural language terms, that guides the similarity search.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Bronchitis-related symptoms') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 3\n),\n\nConditionCTE AS (\n SELECT \n c.PATIENT AS PATIENT, \n c.DESCRIPTION AS DESCRIPTION\n FROM c_filtered AS c\n)\n\nSELECT \n e.ID AS ID, \n e.REASONDESCRIPTION AS REASONDESCRIPTION\nFROM \n ConditionCTE AS cc\nJOIN e_filtered AS e ON toString(cc.PATIENT) = toString(e.PATIENT)\nORDER BY \n e.distance AS distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "List the IDs and reason descriptions for the top 2 encounters related to bronchitis symptoms for patients with acute bronchitis conditions.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0\n\nSELECT \n p.first AS first, \n p.last AS last, \n m.REASONDESCRIPTION AS REASONDESCRIPTION, \n distance(m.REASONDESCRIPTION_embedding, ref_vec_0) AS distance \nFROM \n medications AS m\nJOIN \n patients AS p ON toString(m.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the names of the 3 patients whose medication reasons are most associated with acute bronchitis, along with the descriptions of their medication reasons and how closely these match?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to dust') AS ref_vec_0\n\nSELECT PATIENT, DESCRIPTION, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you find me the top 5 patients who have allergies similar to dust allergies and let me know their names and what their allergies are all about?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common cold (disorder)') AS ref_vec_0\n\nSELECT ITEM, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, could you find the top item in the database that is most like the common cold disorder? I need to know what it is and how similar it is.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0\n\nSELECT START, DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM conditions\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Return the top 5 conditions related to acute bronchitis, including their start times and descriptions.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Cystitis and respiratory conditions') AS ref_vec_0\n\nSELECT c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions AS c\nJOIN all_prevalences AS p ON toString(c.DESCRIPTION) = toString(p.ITEM)\nWHERE p.PREVALENCE_RATE > 0.05\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top three medical conditions related to cystitis and respiratory issues that have a prevalence rate greater than 5%, and present their descriptions.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common surgical procedure') AS ref_vec_0\n\nSELECT distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you find the top 5 procedures that are closest to being a common surgical procedure? Just need their similarity distances.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Recommendation to manage chronic conditions') AS ref_vec_0\n\nSELECT c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans c\nJOIN conditions con ON toString(c.PATIENT) = toString(con.PATIENT)\nWHERE con.DESCRIPTION = 'Chronic condition'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey, can you find the 5 best care plan descriptions out there that are aimed at helping manage chronic conditions? Make sure these are for patients who have a condition marked as \"Chronic condition\".", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Illness Analysis') AS ref_vec_0\n\nSELECT p.patient, distance(ap.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences ap\nJOIN patients p ON toString(ap.POPULATION_TYPE) = toString(p.race)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Who are the top 3 patients associated with chronic illness analysis?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'common cold') AS ref_vec_0\n\nSELECT ITEM, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the item from the `all_prevalences` table that is most representative of the common cold, and return that item.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0,\n\nConditionMatch AS (\n SELECT c.PATIENT, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions AS c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.first, p.last\nFROM ConditionMatch AS cm\nJOIN patients AS p ON toString(cm.PATIENT) = toString(p.patient)\nORDER BY cm.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the first and last names of the ten patients whose medical conditions are most relevant to \"Chronic obstructive pulmonary disease\".", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Bronchitis symptoms') AS ref_vec_0,\n\nRecentEncounters AS (\n SELECT PATIENT, DATE\n FROM encounters\n WHERE DATE > '2023-01-01'\n),\n\nSimilarConditions AS (\n SELECT c.PATIENT, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions AS c\n JOIN RecentEncounters AS re ON toString(c.PATIENT) = toString(re.PATIENT)\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sc.PATIENT\nFROM SimilarConditions AS sc\nWHERE sc.distance < 0.5\nORDER BY sc.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Identify the patient with a condition most similar to \"Bronchitis symptoms\" since January 1, 2023, with a similarity distance under 0.5.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Prescription medication to manage blood pressure') AS ref_vec_0\n\nSELECT m.CODE, distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications AS m\nJOIN patients AS p ON toString(m.PATIENT) = toString(p.patient)\nWHERE p.gender = 'Female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the codes for the top 5 medications prescribed to female patients that are most related to managing blood pressure?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_0\n\nSELECT ID, DESCRIPTION, distance(careplans.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID and description of the care plan that is most related to respiratory therapy?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Recommendation for cardiovascular exercise') AS ref_vec_0\n\nSELECT p.first, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM careplans AS c \nJOIN patients AS p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you fetch me the first names of the 5 patients whose care plans are all about recommending cardiovascular exercise?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up for cold symptoms') AS ref_vec_0\n\nSELECT p.first, e.DESCRIPTION, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM patients p\nJOIN encounters e ON toString(p.patient) = toString(e.PATIENT)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I'm interested in finding some information about patient visits.\"\n**Assistant**: \"Sure, what specific type of visits are you looking for?\"\n**User**: \"I want to know about routine check-ups, especially for cold symptoms.\"\n**Assistant**: \"Got it. How many such visits would you like to see?\"\n**User**: \"I'd like to see the top 5 visits that match this description.\"\n**Assistant**: \"Alright, I'll gather the first names of the patients and the descriptions of their encounters that most closely represent routine check-ups for cold symptoms. Is there anything else you need?\"\n**User**: \"No, that's all for now.\"\n**Assistant**: \"Okay, I will translate your request into an SQL query to find the top 5 relevant patient encounters.\"", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Sinusitis') AS ref_vec_0\n\nSELECT ITEM, PREVALENCE_RATE, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance\nFROM all_prevalences\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the three medical conditions or health-related items that are most relevant to \"Chronic Sinusitis\" and provide their prevalence rates.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Outpatient Encounter') AS ref_vec_0\n\nSELECT e.ID, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters AS e\nJOIN patients AS p ON toString(e.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you provide the IDs and distances for the top 5 outpatient encounters, based on their description's semantic alignment with the term \"Outpatient Encounter\"?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic respiratory condition') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ncp_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 5\n),\n\nConditionMatches AS (\n SELECT \n c.PATIENT AS PATIENT, \n c.DESCRIPTION AS DESCRIPTION, \n c.distance AS condition_distance\n FROM c_filtered AS c\n),\n\nCareplanMatches AS (\n SELECT \n cp.PATIENT AS PATIENT, \n cp.DESCRIPTION AS DESCRIPTION, \n cp.distance AS careplan_distance\n FROM cp_filtered AS cp\n)\n\nSELECT \n p.first || ' ' || p.last AS patient_name\nFROM \n ConditionMatches cm\nJOIN \n CareplanMatches cpm ON toString(cm.PATIENT) = toString(cpm.PATIENT)\nJOIN \n patients p ON toString(cm.PATIENT) = toString(p.patient)\nWHERE \n cm.condition_distance < 0.5 \n AND cpm.careplan_distance < 0.5\nORDER BY \n cm.condition_distance + cpm.careplan_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "**\nPlease find the patient with the most relevant combination of a chronic respiratory condition and a respiratory therapy care plan. Make sure to check that their combined similarity score for condition and care plan is less than 0.5, and only return the top result.\n**", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Medication for birth control') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(medications.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "Can you find a handful of medications that are most associated with birth control and note how closely they match?", + "external_knowledge": "- The 'MATCH' operator in SQLite's vector extension performs an approximate nearest neighbor (ANN) search to find items that are semantically similar to a given concept.\n- The 'lembed()' function is used to transform text into vector embeddings that capture semantic meanings.\n- The 'k = 5' clause specifies that the query should return the top 5 results based on similarity.\n- The lower the distance value, the higher the similarity of the medication descriptions to the concept of \"Medication for birth control\".", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Ultrasound imaging procedure') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I want to find information about medical procedures.\n**Assistant**: Which particular procedure are you interested in?\n**User**: I'm looking for details about ultrasound imaging procedures.\n**Assistant**: Would you like to find the most relevant description for it?\n**User**: Yes, that would be great.\n**Assistant**: Alright, I'll retrieve the top match for an ultrasound imaging procedure from our records.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Influenza vaccine') AS ref_vec_0\n\nSELECT DATE, distance(immunizations.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM immunizations\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 dates when immunizations closely related to the Influenza vaccine occurred?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis disorder') AS ref_vec_0,\n\nprocedure_matches AS (\n SELECT p.PATIENT, p.DESCRIPTION, distance(p.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM procedures p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT ap.ITEM, ap.PREVALENCE_RATE, pm.distance\nFROM all_prevalences ap\nJOIN procedure_matches pm ON toString(ap.ITEM) = toString(pm.DESCRIPTION)\nWHERE ap.PREVALENCE_RATE > 0.05\nORDER BY pm.distance\nLIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "I'm interested in identifying the top 10 medical procedures most relevant to \"Acute bronchitis disorder\" for patients, with a prevalence rate greater than 5%. Could you provide the items and their prevalence rates, sorted by the relevance of their descriptions?", + "external_knowledge": "", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Annual physical examination') AS ref_vec_0,\n\nSimilarEncounters AS (\n SELECT e.ID, e.PATIENT, e.DESCRIPTION, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters e\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.first, p.last\nFROM patients p\nJOIN SimilarEncounters se ON toString(p.patient) = toString(se.PATIENT)\nORDER BY se.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Who are the top 10 patients related to an annual physical examination based on encounter descriptions?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Tree pollen allergy') AS ref_vec_0,\n\nAllergyMatches AS (\n SELECT \n a.PATIENT AS PATIENT, \n a.DESCRIPTION AS DESCRIPTION, \n distance(a.DESCRIPTION_embedding, ref_vec_0) AS allergy_distance\n FROM \n allergies a\n ORDER BY allergy_distance\n LIMIT 5\n)\n\nSELECT \n am.PATIENT AS PATIENT, \n ap.PREVALENCE_RATE AS PREVALENCE_RATE\nFROM \n AllergyMatches am\nJOIN \n all_prevalences ap ON toString(ap.ITEM) = toString(am.DESCRIPTION)\nWHERE \n ap.POPULATION_TYPE = 'general'\nORDER BY \n am.allergy_distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you provide the prevalence rates for the general population of the top 5 allergies that are most similar to tree pollen allergy, and list the patients associated with these allergies in order of their similarity?", + "external_knowledge": "", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergic reaction to pollen') AS ref_vec_0\n\nSELECT START, PATIENT, DESCRIPTION, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you find the most relevant allergy case related to an allergic reaction to pollen, providing the start date, patient details, and description of the allergy?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0\n\nSELECT p.REASONDESCRIPTION, distance(p.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures p\nJOIN encounters e ON toString(p.ENCOUNTER) = toString(e.ID)\nWHERE e.CODE = 12345\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Please provide the descriptions of the top 3 procedures that are most relevant to a standard pregnancy test, specifically for encounters with the code 12345, sorted by their similarity.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Common conditions affecting both genders') AS ref_vec_0,\n\nConditionMatches AS (\n SELECT c.PATIENT, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions c\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.gender\nFROM patients p\nJOIN ConditionMatches cm ON toString(p.patient) = toString(cm.PATIENT)\nORDER BY cm.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Determine the gender of the patient whose medical conditions are among the top 5 most representative of common conditions affecting both genders, and identify the one that matches most closely.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'NuvaRing 0.12/0.015 MG per 24HR 21 Day Vaginal Ring') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(medications.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM medications\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the medication description that is most representative of \"NuvaRing 0.12/0.015 MG per 24HR 21 Day Vaginal Ring\" and let me know what it says? I need only the closest match!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to pollen') AS ref_vec_0\n\nSELECT p.patient, p.first, distance(a.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies a\nJOIN patients p ON toString(a.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you tell me the names and IDs of the top 5 patients who have an allergy most associated with pollen?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 3\n),\n\ncp_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantConditions AS (\n SELECT c.PATIENT, c.DESCRIPTION, c.distance\n FROM c_filtered AS c\n),\n\nPatientDemographics AS (\n SELECT p.patient, p.first, p.last\n FROM patients p\n WHERE p.race = 'White' AND p.ethnicity = 'Non-Hispanic'\n)\n\nSELECT rc.DESCRIPTION AS ConditionDescription, cp.DESCRIPTION AS CarePlanDescription\nFROM RelevantConditions rc\nJOIN cp_filtered AS cp ON toString(rc.PATIENT) = toString(cp.PATIENT)\nJOIN PatientDemographics pd ON toString(rc.PATIENT) = toString(pd.patient)\nORDER BY cp.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you help me find the top 5 care plans and conditions that deal with \"Chronic obstructive pulmonary disease\" and \"Respiratory therapy\"? I need this info for White and Non-Hispanic patients, and make sure you get the ones that are closest matches!", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Patient experiencing acute bronchitis symptoms') AS ref_vec_0\n\nSELECT e.ID, e.DESCRIPTION, e.DATE, p.first || ' ' || p.last AS patient_name, c.DESCRIPTION AS condition_description, ap.PREVALENCE_RATE, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters e\nJOIN patients p ON toString(e.PATIENT) = toString(p.patient)\nJOIN conditions c ON toString(e.ID) = toString(c.ENCOUNTER)\nJOIN all_prevalences ap ON toString(c.DESCRIPTION) = toString(ap.ITEM)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 7, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Provide the IDs, descriptions, dates, patient names, condition descriptions, prevalence rates, and similarity distances for the top 5 medical encounters most relevant to a patient experiencing acute bronchitis symptoms.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Peanut allergy reaction') AS ref_vec_0\n\nSELECT START, distance(allergies.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM allergies\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the start time and distance for the allergy that is most relevant to a peanut allergy reaction?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic Disease Management') AS ref_vec_0\n\nSELECT c.ID, c.DESCRIPTION, p.first, p.last, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM careplans c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nWHERE p.last = 'Smith'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the IDs, descriptions, and distances of the top 5 care plans related to \"Chronic Disease Management\" for patients with the last name Smith? Also, include their first and last names, ordered by their relevance.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up and preventive health measures') AS ref_vec_0,\n\nSimilarEncounters AS (\n SELECT e.ID, e.DESCRIPTION, e.PATIENT, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters AS e\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.first || ' ' || p.last AS full_name\nFROM SimilarEncounters AS se\nJOIN patients AS p ON toString(se.PATIENT) = toString(p.patient)\nORDER BY se.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm trying to locate some patient records.\n**Assistant**: What kind of patient records are you looking to find?\n**User**: I need details on patients who've had routine check-ups and preventive health measures.\n**Assistant**: How many of these records do you need?\n**User**: I'd like the top 5 records that closely match this description.\n**Assistant**: Would you like the patient names sorted by how closely they match this description?\n**User**: Yes, please sort them by their similarity.\n**Assistant**: Got it. I will help you find the names of the 5 patients associated with encounters most similar to routine check-ups and preventive measures, ordered by their closeness to this description.", + "external_knowledge": "", + "integration_level": 2, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Annual check-up for general health') AS ref_vec_0\n\nSELECT ID, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you provide me with the IDs and similarity distances for the top 5 encounters related to an annual check-up for general health?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up and monitoring') AS ref_vec_0\n\nSELECT ID, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "**\n\nCan you provide the IDs of the 5 encounters that best match the description of a routine check-up and monitoring?\n\n**", + "external_knowledge": "", + "integration_level": 2, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine checkup procedure for cardiovascular health') AS ref_vec_0\n\nSELECT p.first, p.last, distance(pr.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures pr\nJOIN patients p ON toString(pr.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Find the names of the top 5 patients who had procedures similar to a routine cardiovascular health checkup.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function (procedure)') AS ref_vec_0\n\nSELECT DATE, DESCRIPTION, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Retrieve the dates and descriptions of the top 10 procedures related to measuring respiratory function.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine checkup') AS ref_vec_0\n\nSELECT distance(procedures.REASONDESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Uncover the distance to the nearest star in the galaxy of routine medical checkups.", + "external_knowledge": "The `MATCH` operator is used for approximate nearest neighbor (ANN) search in vector databases, where entries are compared based on their distance to a given vector. The vector embedding `lembed('all-MiniLM-L6-v2', \"Routine checkup\")` transforms the text \"Routine checkup\" into a numeric representation, allowing the database to perform similarity searches. The `k=1` parameter specifies that the query should return the closest match, with lower distance values indicating higher similarity to the query vector. Typically, Euclidean distance is used to measure the similarity between vectors.", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic bronchitis (disorder)') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM conditions\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: \"I'm interested in finding certain medical conditions.\"\n**Assistant**: \"What specific conditions are you looking for?\"\n**User**: \"I'm particularly interested in conditions related to chronic bronchitis.\"\n**Assistant**: \"How many conditions would you like information on?\"\n**User**: \"I'd like to know about the top 5 conditions.\"\n**Assistant**: \"Alright, I will find and provide you the descriptions of the 5 conditions that are most related to chronic bronchitis.\"", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Penicillin V Potassium 250 MG') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Routine check-up') AS ref_vec_1,\n\nmedications_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM medications\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantMedications AS (\n SELECT \n PATIENT,\n DESCRIPTION,\n distance\n FROM medications_filtered AS medications\n)\n\nSELECT DISTINCT e.ID\nFROM e_filtered AS e\nJOIN RelevantMedications rm ON toString(e.PATIENT) = toString(rm.PATIENT)\nORDER BY e.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'm interested in some medical records.\n**Assistant**: What kind of medical records are you looking for?\n**User**: I'm particularly curious about patients who have received certain medications.\n**Assistant**: Which medications are you interested in?\n**User**: Medications similar to Penicillin V Potassium 250 MG.\n**Assistant**: How many medication records would you like to focus on?\n**User**: Let's look at the top 5 relevant ones.\n**Assistant**: Alright. Now, are there specific types of encounters you're interested in for these patients?\n**User**: Yes, I'm interested in routine check-ups.\n**Assistant**: How many of these routine check-up encounters would you like to examine?\n**User**: The top 3 encounters would be sufficient.\n**Assistant**: Great, I'll help you find the IDs of these encounters, sorted by relevance to your criteria.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0\n\nSELECT p.DATE, distance(p.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures AS p\nJOIN encounters AS e ON toString(p.ENCOUNTER) = toString(e.ID)\nWHERE e.REASONCODE = 12345\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "What are the dates for a few procedures related to a standard pregnancy test, especially if they involve reason code 12345?", + "external_knowledge": "Vector similarity searches in SQL use approximate nearest neighbor techniques to find items that closely match a given vector. The `MATCH` operator compares vector embeddings, and the `lembed()` function generates embeddings based on specified text inputs. In this context, the \"all-MiniLM-L6-v2\" model is used to create embeddings for textual similarity. The `k = 3` parameter specifies that only the top three most similar procedures are retrieved, while the Euclidean distance functions as the similarity metric—lower distances denote higher similarity. This approach is particularly useful in identifying items that are conceptually related, even if they are not identical in description.", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine check-up at the pediatric clinic') AS ref_vec_0\n\nSELECT ID, DATE, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the top 3 encounters that are just like a routine check-up at the pediatric clinic? I'd love to know their IDs and when they happened.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function (procedure)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM allergies\n\n ORDER BY distance\n LIMIT 5\n),\n\nPatientEncounters AS (\n SELECT \n e.ID AS encounter_id,\n p.patient AS patient,\n e.DATE AS DATE,\n e.DESCRIPTION AS DESCRIPTION,\n e.REASONDESCRIPTION AS REASONDESCRIPTION,\n e.distance AS distance\n FROM e_filtered AS e\n JOIN \n patients p ON toString(e.PATIENT) = toString(p.patient)\n ORDER BY \n e.distance AS distance\n),\n\nAllergyEncounters AS (\n SELECT \n a.ENCOUNTER AS encounter_id,\n a.PATIENT AS PATIENT,\n a.START AS START,\n a.DESCRIPTION AS DESCRIPTION,\n a.distance AS distance\n FROM a_filtered AS a\n ORDER BY \n a.distance AS distance\n)\n\nSELECT \n pe.encounter_id AS encounter_id,\n ae.START AS START\nFROM \n PatientEncounters pe\nJOIN \n AllergyEncounters ae ON toString(pe.encounter_id) = toString(ae.encounter_id)\nWHERE \n pe.DATE > '2022-01-01'\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which ten recent medical encounters involved procedures related to respiratory function and were associated with bronchitis?", + "external_knowledge": "The query uses `MATCH` with `lembed()` to perform an approximate nearest neighbor (ANN) search, which is a type of vector search. This method compares vector embeddings of text descriptions to find the top N items (in this case, N=5) that are most similar to a specified concept. The `lembed('all-MiniLM-L6-v2', ...)` function generates embeddings for the specified text using a specific language model. The similarity between items is calculated using the Euclidean distance (L2 norm), where a smaller distance indicates higher similarity. In this context, the search is looking for the top 5 encounters that are most similar to the concepts of \"Measurement of respiratory function (procedure)\" and \"Acute bronchitis (disorder)\".", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic cough medication') AS ref_vec_0\n\nSELECT \n p.first AS patient_first_name,\n p.last AS patient_last_name,\n m.DESCRIPTION AS medication_description,\n a.PREVALENCE_RATE AS prevalence_rate,\n distance(m.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications AS m\nJOIN patients AS p ON toString(m.PATIENT) = toString(p.patient)\nJOIN all_prevalences AS a ON toString(m.CODE) = toString(a.ITEM)\nWHERE a.POPULATION_TYPE = 'general population'\nAND a.PREVALENCE_RATE > 0.05\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 5, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you please find the top 3 medications for chronic cough and give me the patients' full names and the prevalence rates? Make sure these are for the general population and only include those with a prevalence rate over 0.05!", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Antibiotic for bacterial infection') AS ref_vec_0\n\nSELECT START, PATIENT, distance(medications.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM medications\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the start dates, patient details, and similarity distances for the top 5 medications that are most relevant to antibiotics used for bacterial infections?", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Cystitis') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n WHERE DATE BETWEEN '2023-01-01' AND '2023-12-31'\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT c.DESCRIPTION, e.REASONDESCRIPTION\nFROM c_filtered AS c\nJOIN e_filtered AS e ON toString(c.ENCOUNTER) = toString(e.ID)\nORDER BY c.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find a few instances from 2023 where the condition seems like Cystitis and the reason given sounds like Acute bronchitis? List their descriptions and reasons.", + "external_knowledge": "In this context, vector similarity search is used to find text records that are semantically close to specified phrases. The `MATCH lembed('all-MiniLM-L6-v2', ...)` function utilizes an approximate nearest neighbor (ANN) search to find the closest matches to the given descriptions based on their vector representations. The parameter `k` specifies how many similar items to retrieve for each search. The `c.k = 5` and `e.k = 3` parameters indicate that the top 5 and top 3 similar entries should be retrieved for \"Cystitis\" and \"Acute bronchitis (disorder)\", respectively. The results are ordered by distance, and lower distances indicate higher similarity in the vector space. The `lembed('all-MiniLM-L6-v2', ...)` function converts text into a vector embedding using a pre-trained language model. This query uses Euclidean distance as the metric for similarity.", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Viral Sinusitis (Disorder)') AS ref_vec_0,\n\nFilteredPatients AS (\n SELECT patient, race, ethnicity\n FROM patients\n WHERE race = 'White' AND ethnicity = 'Non-Hispanic'\n),\n\nVectorSearchResults AS (\n SELECT ITEM, POPULATION_TYPE, OCCURRENCES, POPULATION_COUNT, PREVALENCE_RATE, distance(all_prevalences.ITEM_embedding, ref_vec_0) AS distance\n FROM all_prevalences\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT v.ITEM\nFROM VectorSearchResults v\nJOIN FilteredPatients p ON toString(v.POPULATION_TYPE) = toString(p.race)\nWHERE v.distance < 0.2\nORDER BY v.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the realm of health, can you uncover the most crucial prevalence factor linked to the race of patients who are considered 'White', with regards to battling the storm known as Viral Sinusitis?", + "external_knowledge": "For vector operations, the query utilizes the `MATCH` operator within the `lembed` function to perform approximate nearest neighbor (ANN) search, which identifies items that are most similar to a given concept based on vector embeddings. The number `k = 10` specifies that the query initially targets the top 10 similar items. Vectors are compared using Euclidean distance, and a lower distance signifies higher similarity. The concept of \"Viral Sinusitis (Disorder)\" is likely embedded in a semantic space, allowing for nuanced comparisons based on contextual meaning rather than exact text matching.", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Measurement of respiratory function (procedure)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_1,\n lembed('all-MiniLM-L6-v2', 'Respiratory disorder') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM procedures\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 5\n),\n\nco_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_2) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\nProcedureMatches AS (\n SELECT p.DATE, p.PATIENT, p.ENCOUNTER, p.CODE, p.DESCRIPTION, p.distance\n FROM p_filtered AS p\n),\n\nCarePlanMatches AS (\n SELECT c.START, c.STOP, c.PATIENT, c.ENCOUNTER, c.CODE, c.DESCRIPTION, c.REASONCODE, c.REASONDESCRIPTION, c.distance\n FROM c_filtered AS c\n),\n\nConditionMatches AS (\n SELECT co.START, co.STOP, co.PATIENT, co.ENCOUNTER, co.CODE, co.DESCRIPTION, co.distance\n FROM co_filtered AS co\n)\n\nSELECT pm.DATE\nFROM ProcedureMatches pm\nJOIN CarePlanMatches cm ON toString(pm.PATIENT) = toString(cm.PATIENT)\nJOIN ConditionMatches com ON toString(pm.PATIENT) = toString(com.PATIENT)\nWHERE pm.distance < 0.5 AND cm.distance < 0.5 AND com.distance < 0.5\nORDER BY pm.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you find the earliest date when a patient had a procedure really relevant to measuring respiratory function, had a care plan related to acute bronchitis, and had a condition about a respiratory disorder? Make sure all these matches are super relevant, with distances less than 0.5!", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Check-up for flu-like symptoms') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Plan for treating acute respiratory conditions') AS ref_vec_1,\n\nencounters_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 10\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 5\n),\n\nRecentEncounters AS (\n SELECT ID, PATIENT, REASONDESCRIPTION_embedding\n FROM encounters_filtered AS encounters\n)\n\nSELECT DISTINCT c.DESCRIPTION\nFROM c_filtered AS c\nJOIN RecentEncounters AS e ON toString(c.PATIENT) = toString(e.PATIENT)\nORDER BY c.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the distinct care plan descriptions for patients who recently visited for check-ups concerning flu-like symptoms. Ensure these care plans align closely with treating acute respiratory conditions, and provide the top 5 most relevant plans ordered by similarity.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease (disorder)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Routine check-up') AS ref_vec_1,\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT c.distance\nFROM c_filtered AS c\nJOIN e_filtered AS e ON toString(c.ENCOUNTER) = toString(e.ID)\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\n WHERE p.ethnicity = 'Hispanic or Latino' AND p.gender = 'Female' ORDER BY c.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 5 conditions related to \"Chronic obstructive pulmonary disease\" for Hispanic or Latino female patients? I’d like to know how closely these conditions match a routine check-up. Thanks!", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease') AS ref_vec_0,\n\nConditionMatches AS (\n SELECT c.PATIENT, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance \n FROM conditions AS c\n ORDER BY distance\n LIMIT 5\n),\n\nEncounterDetails AS (\n SELECT e.PATIENT, e.DESCRIPTION AS Encounter_Description\n FROM encounters AS e\n JOIN ConditionMatches AS cm ON toString(e.PATIENT) = toString(cm.PATIENT)\n WHERE e.DATE > '2023-01-01'\n)\n\nSELECT p.first AS FirstName, p.last AS LastName\nFROM patients AS p\nJOIN EncounterDetails AS ed ON toString(p.patient) = toString(ed.PATIENT)\nORDER BY ed.Encounter_Description\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Find the first and last names of up to 10 patients who had encounters after January 1, 2023, and are among the top 5 related to \"Chronic obstructive pulmonary disease.\"", + "external_knowledge": "", + "integration_level": 3, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Standard pregnancy test') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Multi-turn Dialogue", + "question": "**User**: I'd like to find some procedures related to pregnancy tests.\n**Assistant**: Could you specify which aspect or type of pregnancy test you're interested in?\n**User**: I'm looking for standard pregnancy tests.\n**Assistant**: How many procedures would you like to find?\n**User**: I need the top 5 related procedures.\n**Assistant**: Anything else you need along with this list?\n**User**: I'd like to see how similar they are in relation to the standard pregnancy test concept.\n**Assistant**: OK, I will help you translate your request into an SQL query.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Cystitis') AS ref_vec_0\n\nSELECT distance(conditions.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the 5 most relevant conditions related to \"Cystitis\" and provide their distances? I'm really curious about this information!", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory therapy session') AS ref_vec_0,\n\nEncounterCTE AS (\n SELECT ID, DESCRIPTION, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT e.ID, p.first\nFROM EncounterCTE e\nJOIN patients p ON toString(e.ID) = toString(p.patient)\nORDER BY e.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the encounters most relevant to a \"Respiratory therapy session\" and list the first names of the associated patients. Provide information for the top 5 encounters ordered by similarity.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Allergy to pollen') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Asthma management plan') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM allergies\n\n ORDER BY distance\n LIMIT 5\n),\n\nc_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM careplans\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n p.patient AS patient_id, \n a.DESCRIPTION AS allergy_description, \n c.DESCRIPTION AS careplan_description, \n op.VALUE AS observation_value\nFROM patients p\nJOIN a_filtered AS a ON toString(p.patient) = toString(a.PATIENT)\nJOIN c_filtered AS c ON toString(p.patient) = toString(c.PATIENT)\nJOIN observations op ON toString(p.patient) = toString(op.PATIENT)\nJOIN all_prevalences ap ON toString(a.CODE) = toString(ap.ITEM)\n WHERE ap.PREVALENCE_RATE > 0.1 ORDER BY a.distance;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you provide the patient IDs, allergy descriptions, care plan descriptions, and observation values for the 5 patients who have allergies related to pollen and are on an asthma management plan, ensuring that these allergies have a prevalence rate above 0.1 and are ordered by relevance to pollen allergy?", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Chronic obstructive pulmonary disease (COPD)') AS ref_vec_0\n\nSELECT p.first, p.last, c.DESCRIPTION, distance(c.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM conditions c\nJOIN patients p ON toString(c.PATIENT) = toString(p.patient)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the names and condition descriptions of the top 5 patients whose medical conditions are most related to \"Chronic obstructive pulmonary disease (COPD)\", and order them by their similarity to this condition?", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Respiratory function measurement') AS ref_vec_0\n\nSELECT DATE, PATIENT, ENCOUNTER, distance(procedures.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM procedures\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the top 5 procedures related to respiratory function measurement and provide their dates, patient identifiers, encounter details, and similarity distances.", + "external_knowledge": "", + "integration_level": 1, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Consultation related to influenza symptoms') AS ref_vec_0\n\nSELECT e.DESCRIPTION, p.gender, distance(e.DESCRIPTION_embedding, ref_vec_0) AS distance \nFROM encounters AS e\nJOIN patients AS p ON toString(e.PATIENT) = toString(p.patient)\nWHERE p.gender = 'female'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Return the descriptions and genders of the top 5 female patients' encounters related to influenza consultations.", + "external_knowledge": "", + "integration_level": 5, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Routine health check-up and examination') AS ref_vec_0\n\nSELECT DESCRIPTION, distance(encounters.DESCRIPTION_embedding, ref_vec_0) AS distance\nFROM encounters\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the medical encounter description that best represents a routine health check-up and examination.", + "external_knowledge": "", + "integration_level": 2, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Acute bronchitis (disorder)') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Bronchitis') AS ref_vec_1,\n\nconditions_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_0) AS distance\n FROM conditions\n\n ORDER BY distance\n LIMIT 5\n),\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_1) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nAcuteBronchitisConditions AS (\n SELECT PATIENT, START, STOP, ENCOUNTER\n FROM conditions_filtered AS conditions\n),\n\nBronchitisEncounters AS (\n SELECT e.ID, e.DATE, e.PATIENT, e.DESCRIPTION, e.REASONDESCRIPTION, e.distance\n FROM e_filtered AS e\n JOIN AcuteBronchitisConditions abc ON toString(e.PATIENT) = toString(abc.PATIENT)\n ORDER BY e.distance\n LIMIT 10\n)\n\nSELECT ap.PREVALENCE_RATE\nFROM all_prevalences ap\nJOIN BronchitisEncounters be ON toString(ap.ITEM) = toString(be.REASONDESCRIPTION)\nWHERE ap.POPULATION_TYPE = 'General'\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "For patients with the top 5 conditions related to acute bronchitis, find the prevalence rate of bronchitis in the general population based on their top 5 related encounters.", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + }, + { + "db_id": "synthea", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Severe viral infection requiring immediate attention') AS ref_vec_0,\n lembed('all-MiniLM-L6-v2', 'Antiviral medication used for acute treatment') AS ref_vec_1,\n\ne_filtered AS (\n SELECT\n *,\n distance(REASONDESCRIPTION_embedding, ref_vec_0) AS distance\n FROM encounters\n\n ORDER BY distance\n LIMIT 5\n),\n\nm_filtered AS (\n SELECT\n *,\n distance(DESCRIPTION_embedding, ref_vec_1) AS distance\n FROM medications\n\n ORDER BY distance\n LIMIT 10\n),\n\nEncounterMatch AS (\n SELECT e.ID, e.PATIENT, e.distance\n FROM e_filtered AS e\n)\n\nSELECT m.PATIENT, m.CODE\nFROM m_filtered AS m\nJOIN EncounterMatch em ON toString(m.PATIENT) = toString(em.PATIENT)\nORDER BY m.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the top 5 antiviral medications prescribed to patients who faced severe viral infections, requiring immediate attention?", + "external_knowledge": "", + "integration_level": 9, + "execution_status": "success", + "sql_candidate": [], + "db_type": "myscale", + "schema": "CREATE TABLE all_prevalences (\n `ITEM` Nullable(String),\n `POPULATION_TYPE` Nullable(String),\n `OCCURRENCES` Nullable(Int64),\n `POPULATION_COUNT` Nullable(Int64),\n `PREVALENCE_RATE` Nullable(Float64),\n `PREVALENCE_PERCENTAGE` Nullable(Float64),\n `ITEM_embedding` Array(Float32)\n);\nCREATE TABLE allergies (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE careplans (\n `ID` Nullable(String),\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Float64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE claims (\n `ID` Nullable(String),\n `PATIENT` Nullable(String),\n `BILLABLEPERIOD` Nullable(Date),\n `ORGANIZATION` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `DIAGNOSIS` Nullable(String),\n `TOTAL` Nullable(Int64)\n);\nCREATE TABLE conditions (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE encounters (\n `ID` Nullable(String),\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE immunizations (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE medications (\n `START` Nullable(String),\n `STOP` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);\nCREATE TABLE observations (\n `DATE` Nullable(Date),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(String),\n `DESCRIPTION` Nullable(String),\n `VALUE` Nullable(Float64),\n `UNITS` Nullable(String)\n);\nCREATE TABLE patients (\n `patient` Nullable(String),\n `birthdate` Nullable(Date),\n `deathdate` Nullable(Date),\n `ssn` Nullable(String),\n `drivers` Nullable(String),\n `passport` Nullable(String),\n `prefix` Nullable(String),\n `first` Nullable(String),\n `last` Nullable(String),\n `suffix` Nullable(String),\n `maiden` Nullable(String),\n `marital` Nullable(String),\n `race` Nullable(String),\n `ethnicity` Nullable(String),\n `gender` Nullable(String),\n `birthplace` Nullable(String),\n `address` Nullable(String)\n);\nCREATE TABLE procedures (\n `DATE` Nullable(String),\n `PATIENT` Nullable(String),\n `ENCOUNTER` Nullable(String),\n `CODE` Nullable(Int64),\n `DESCRIPTION` Nullable(String),\n `REASONCODE` Nullable(Int64),\n `REASONDESCRIPTION` Nullable(String),\n `DESCRIPTION_embedding` Array(Float32),\n `REASONDESCRIPTION_embedding` Array(Float32)\n);" + } +] \ No newline at end of file diff --git a/benchmark/data/results/wikipedia_multimodal/candidate_sql.json b/benchmark/data/results/wikipedia_multimodal/candidate_sql.json new file mode 100644 index 0000000..8554908 --- /dev/null +++ b/benchmark/data/results/wikipedia_multimodal/candidate_sql.json @@ -0,0 +1,2705 @@ +[ + { + "db_id": "wikipedia_multimodal", + "sql": "WITH RelevantArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE raw_html_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"solar energy buildings in Millennium Park\") AND k = 5\n)\n\n\nSELECT p.text\nFROM Paragraphs p\nJOIN RelevantArticles ra ON p.article_id = ra.article_id\nWHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"electricity generation from solar power\") AND p.k = 3\nORDER BY p.distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you present the top 10 paragraphs from the 5 most relevant articles about solar energy buildings in Millennium Park, focusing on their connection to electricity generation from solar power?", + "external_knowledge": "", + "sql_candidate": [ + "WITH RelevantArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE raw_html_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"solar energy buildings in Millennium Park\") AND k = 5\n)\n\n\nSELECT p.text\nFROM Paragraphs p\nJOIN RelevantArticles ra ON p.article_id = ra.article_id\nWHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"electricity generation from solar power\") AND p.k = 3\nORDER BY p.distance\nLIMIT 10;" + ], + "execution_status": "exception", + "error_message": "歧义错误: 在多表查询中发现无别名的向量搜索列 'text_embedding'。请为该列表明表别名。", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "SELECT title FROM Articles;", + "sql_result_column_count": 1, + "sql_result_rows_count": 100, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you give me the list of all the article titles you've got in the database?", + "external_knowledge": "", + "sql_candidate": [ + "SELECT title FROM Articles;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Featured article about energy-efficient buildings in Chicago') AS ref_vec_0\n\nSELECT title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you find the top article that discusses energy-efficient buildings in Chicago and give me its title?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Featured article about energy-efficient buildings in Chicago') AS ref_vec_0\n\nSELECT title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed exploration of modern renewable energy solutions and their impact on global economies.') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Current advancements in renewable energy technologies and their economic implications.') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy advancements') AS ref_vec_2,\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed exploration of modern renewable energy solutions\n ORDER BY distance\n LIMIT 10\n),\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n WHERE article_id IN (SELECT article_id FROM ParagraphMatch) AND raw_wikitext_embedding MATCH lembed(''laion/CLIP-ViT-B-32-laion2B-s34B-b79K'', ''Current advancements in renewable energy technologies\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_2) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nParagraphMatch AS (\n SELECT paragraph_id, article_id, distance\n FROM Paragraphs_filtered AS Paragraphs their impact on global economies.')\n),\n\nArticleMatch AS (\n SELECT article_id, title, distance\n FROM Articles_filtered AS Articles their economic implications.')\n),\n\nImageAggregation AS (\n SELECT GROUP_CONCAT(i.caption, ', ') AS aggregated_captions\n FROM i_filtered AS i\n JOIN ArticleMatch a ON toString(i.article_id) = toString(a.article_id)\n)\n\nSELECT aggregated_captions\nFROM ImageAggregation;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the top 3 image captions related to renewable energy advancements, aggregated from articles discussing modern renewable energy solutions and their economic impact?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed exploration of modern renewable energy solutions and their impact on global economies.') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Current advancements in renewable energy technologies and their economic implications.') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy advancements') AS ref_vec_2,\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed exploration of modern renewable energy solutions\n ORDER BY distance\n LIMIT 10\n),\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n WHERE article_id IN (SELECT article_id FROM ParagraphMatch) AND raw_wikitext_embedding MATCH lembed(''laion/CLIP-ViT-B-32-laion2B-s34B-b79K'', ''Current advancements in renewable energy technologies\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_2) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nParagraphMatch AS (\n SELECT paragraph_id, article_id, distance\n FROM Paragraphs_filtered AS Paragraphs their impact on global economies.')\n),\n\nArticleMatch AS (\n SELECT article_id, title, distance\n FROM Articles_filtered AS Articles their economic implications.')\n),\n\nImageAggregation AS (\n SELECT GROUP_CONCAT(i.caption, ', ') AS aggregated_captions\n FROM i_filtered AS i\n JOIN ArticleMatch a ON toString(i.article_id) = toString(a.article_id)\n)\n\nSELECT aggregated_captions\nFROM ImageAggregation;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 34005 ('') AS aggregated_captions\n FROM i_filtered AS i\n JOIN ArticleMatch a ON toString(i.article_id) = toString(a.article_id)\n)\n\nSELECT aggregated_captions\nFROM ImageAggregation\n FORMAT Native') (line 47, col 39): ') AS aggregated_captions\n FROM i_filtered AS i\n JOIN ArticleMatch a ON toString(i.article_id) = toString(a.article_id)\n)\n\nSELECT aggregated_capti. Single quoted string is not closed: '') AS aggregated_captions\n FROM i_filtered AS i\n JOIN ArticleMatch a ON toString(i.article_id) = toString(a.article_id)\n)\n\nSELECT aggregated_captions\nFROM ImageAggregation\n FORMAT Native'. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe amendment controversy') AS ref_vec_0\n\nSELECT heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you find me the most relevant heading related to the Saxbe amendment controversy? Just need the text of the best one!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe amendment controversy') AS ref_vec_0\n\nSELECT heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'architecture and sustainable design in urban spaces') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you fetch me the article that's all about architecture and sustainable design in urban spaces? I only need the top one, okay?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'architecture and sustainable design in urban spaces') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced architectural design in modern municipal buildings in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Chicago%'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Find the title of the article that is the best match for advanced architectural design in modern municipal buildings in Chicago, and ensure the article is associated with an image description containing the keyword \"Chicago\".", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced architectural design in modern municipal buildings in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Chicago%'\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Can you show me the article ID and title of the article that is most relevant to electricity generation from solar energy?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of Chicago''''s architectural significance in modern history.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you find the paragraph ID that best describes the exploration of Chicago's architectural significance in modern history?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of Chicago''''s architectural significance in modern history.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'ethical conflicts in governance') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you tell me the IDs of the top 5 paragraphs that most closely align with the topic of ethical conflicts in governance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'ethical conflicts in governance') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'sustainable architecture and green building practices') AS ref_vec_0\n\nSELECT p.article_id, p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the article and paragraph IDs for the 5 paragraphs most related to sustainable architecture and green building practices?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'sustainable architecture and green building practices') AS ref_vec_0\n\nSELECT p.article_id, p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into solar energy and architecture in Chicago') AS ref_vec_0,\n\nFilteredParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM FilteredParagraphs fp\nJOIN Articles a ON toString(fp.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 123;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! I'm looking for the top 5 paragraphs from articles on Wikipedia about solar energy and architecture in Chicago. Can you find those for me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into solar energy and architecture in Chicago') AS ref_vec_0,\n\nFilteredParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM FilteredParagraphs fp\nJOIN Articles a ON toString(fp.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 123;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter and Edmund Muskie') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Please identify the image associated with the top caption that most closely represents \"President Carter and Edmund Muskie\" from the Images table.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter and Edmund Muskie') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The President appoints a Congress member avoiding the Ineligibility Clause') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the three paragraphs that are most relevant to the scenario where the President appoints a Congress member while avoiding the Ineligibility Clause, and provide their unique identifiers along with the articles they belong to and the similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The President appoints a Congress member avoiding the Ineligibility Clause') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States Constitution') AS ref_vec_0\n\nSELECT a.title, a.url, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to find the top 3 paragraphs related to historical events in the United States Constitution from various articles. Please provide me with the titles and URLs of these articles, along with the position of each paragraph within its article.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States Constitution') AS ref_vec_0\n\nSELECT a.title, a.url, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States constitutional appointments and Saxbe fix') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nORDER BY rp.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the article title that's most related to \"United States constitutional appointments and Saxbe fix\"? I just need the top one, thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States constitutional appointments and Saxbe fix') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nORDER BY rp.distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A mechanism by which the President of the United States appoints a current or former member of Congress') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title\nFROM ParagraphSearch ps\nJOIN Articles a ON toString(ps.article_id) = toString(a.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles of the 3 articles that most relate to the concept of how the President of the United States appoints a current or former member of Congress?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A mechanism by which the President of the United States appoints a current or former member of Congress') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title\nFROM ParagraphSearch ps\nJOIN Articles a ON toString(ps.article_id) = toString(a.article_id);" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'analysis on solar energy buildings') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url\nFROM Articles a\nJOIN ParagraphSearch ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 5 articles that include paragraphs most relevant to the topic of \"analysis on solar energy buildings\" and provide their titles and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'analysis on solar energy buildings') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url\nFROM Articles a\nJOIN ParagraphSearch ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of climate change impacts and solutions') AS ref_vec_0\n\nSELECT \n a.title AS ArticleTitle, \n a.url AS ArticleURL, \n p.text AS ParagraphText, \n distance(p.text_embedding, ref_vec_0) AS ParagraphDistance\nFROM \n Paragraphs p\n JOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY ParagraphDistance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "What are the top 5 paragraphs related to the exploration of climate change impacts and solutions, including their article titles and URLs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of climate change impacts and solutions') AS ref_vec_0\n\nSELECT \n a.title AS ArticleTitle, \n a.url AS ArticleURL, \n p.text AS ParagraphText, \n distance(p.text_embedding, ref_vec_0) AS ParagraphDistance\nFROM \n Paragraphs p\n JOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY ParagraphDistance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The story unfolds in the bustling city of New York, where characters navigate complex social dynamics.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top 5 paragraphs related to the narrative of a story unfolding in New York City, focusing on complex social dynamics, and provide their paragraph IDs, associated article IDs, and text content.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The story unfolds in the bustling city of New York, where characters navigate complex social dynamics.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy electricity generation') AS ref_vec_0\n\nSELECT a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nWHERE a.wiki_id = 1\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few articles that are about generating electricity with solar energy?", + "external_knowledge": "The query utilizes a vector search mechanism where the `MATCH` operator performs an approximate nearest neighbor (ANN) search, which is used to find the articles most relevant to the phrase \"solar energy electricity generation.\" The `lembed` function with the specified model generates embeddings that capture the semantic meaning of the input text. The search returns the top 5 articles based on their closeness in vector space, indicating their conceptual similarity to the search term. In this context, \"a few\" refers to the limit of 5 articles. For the search to be effective, the embeddings are compared using the Euclidean distance, where a smaller distance indicates higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy electricity generation') AS ref_vec_0\n\nSELECT a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nWHERE a.wiki_id = 1\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of ethical conflicts in US Congress appointments') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which paragraph most closely relates to the exploration of ethical conflicts in US Congress appointments, based on the embeddings?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of ethical conflicts in US Congress appointments') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a solution for appointing members of Congress to civil offices.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT\n paragraph_id,\n article_id,\n text,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM\n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n a.title AS title\nFROM\n Articles a\nJOIN\n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY\n sp.distance AS distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Can you tell me the title of the article that most closely relates to the concept of \"The Saxbe fix as a solution for appointing members of Congress to civil offices,\" based on the top 5 most relevant paragraphs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a solution for appointing members of Congress to civil offices.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT\n paragraph_id,\n article_id,\n text,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM\n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n a.title AS title\nFROM\n Articles a\nJOIN\n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY\n sp.distance AS distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbirdis a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the paragraph ID and article ID for the paragraph most related to \"To Kill a Mockingbird\" by Harper Lee.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbirdis a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix mechanism allows Presidents to appoint current or former Congress members to civil office positions without constitutional restrictions') AS ref_vec_0\n\nSELECT a.article_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Which articles touch upon the mechanism that lets Presidents appoint Congress members to civil positions? Give me a handful of them.", + "external_knowledge": "In vector operations, the `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which helps find items that are most similar to a given concept based on vector embeddings. The `lembed()` function utilizes a specific vector model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`) to encode concepts into vector representations. The SQL query specifies `k=5`, meaning it retrieves the top 5 items that are most similar to the specified concept. This technique is useful for retrieving content that is contextually similar, as the vector comparison generally uses Euclidean distance where similarity increases as distance decreases.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix mechanism allows Presidents to appoint current or former Congress members to civil office positions without constitutional restrictions') AS ref_vec_0\n\nSELECT a.article_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy sources in urban areas') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and sustainability practices') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT p.text\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\n WHERE sustainability practices') ORDER BY p.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Please identify and return the text from the top paragraph that is highly relevant to solar energy and sustainability practices from the five leading articles about renewable energy sources in urban areas. Make sure to order the paragraphs by their relatedness to the topic for optimal relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy sources in urban areas') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and sustainability practices') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT p.text\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\n WHERE sustainability practices') ORDER BY p.distance;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 22047 ('(') (line 15, col 15): (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-l. Unmatched parentheses: (. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A timeless exploration of human resilience and courage.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n paragraph_id\nFROM \n RelevantParagraphs\nORDER BY \n distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the paragraph ID for the top paragraph that captures the essence of human resilience and courage? I'm curious to see which one stands out the most.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A timeless exploration of human resilience and courage.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n paragraph_id\nFROM \n RelevantParagraphs\nORDER BY \n distance;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An iconic representation of historical events with profound impact.') AS ref_vec_0\n\nSELECT i.image_title, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nWHERE h.heading_text = 'Historical Events'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Top 3 images titled related to 'Historical Events' that represent iconic historical impacts.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An iconic representation of historical events with profound impact.') AS ref_vec_0\n\nSELECT i.image_title, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nWHERE h.heading_text = 'Historical Events'\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'caption_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantArticles AS (\n SELECT article_id, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, a.article_id, p.distance\nFROM p_filtered AS p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY p.distance LIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Can you provide the paragraph IDs, article IDs, and their relevance distances for the 10 paragraphs most related to the concept of the \"Saxbe fix\" within the top 5 articles concerning the \"United States Constitution\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantArticles AS (\n SELECT article_id, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, a.article_id, p.distance\nFROM p_filtered AS p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY p.distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of solar energy utilization in modern architecture') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you dig up the top 5 paragraphs that are all about using solar energy in modern buildings? I'd like to know their IDs and how closely they match the topic.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of solar energy utilization in modern architecture') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring constitutional mechanisms to prevent ethical conflicts') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "**\n\nWhat are the IDs of the paragraphs in those couple of articles that delve into constitutional mechanisms for preventing ethical issues?\n\n**", + "external_knowledge": "**\n\nIn the context of vector searches using the `sqlite-lembed` extension, the `MATCH` operator facilitates approximate nearest neighbor searches. This means it identifies data points whose vector representations are closest to a specified reference vector, determined by a given textual concept. The `k=2` clause restricts the results to the two closest matches in terms of vector similarity, computed typically by Euclidean distance. The 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model is used to generate embeddings that capture semantic meaning, allowing for sophisticated querying based on conceptual similarity rather than direct keyword matching.\n\n**", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring constitutional mechanisms to prevent ethical conflicts') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 2;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Taxation and government finance') AS ref_vec_0\n\nSELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Guide me on a journey to uncover the top 5 headings that resonate with the concept of 'Taxation and government finance,' revealing their unique identifiers and the closeness of their connection.", + "external_knowledge": "The query employs the \"MATCH\" operator to perform an approximate nearest neighbor (ANN) search, seeking headings that share a semantic closeness to the phrase \"Taxation and government finance.\" This operation uses vector embeddings to capture semantic meaning, with similarity measured by Euclidean distance (L2 norm). A lower distance indicates a higher degree of similarity. The model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' is designed to handle such tasks efficiently, translating textual concepts into vector space for comparison.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Taxation and government finance') AS ref_vec_0\n\nSELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH ArticleMatches AS (\n SELECT a.article_id, a.title, a.url, a.distance AS article_distance\n FROM Articles a\n WHERE a.raw_html_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"Historical legal procedures in the US government\")\n LIMIT 3\n),\nParagraphMatches AS (\n SELECT p.paragraph_id, p.article_id, p.text, p.distance AS paragraph_distance\n FROM Paragraphs p\n WHERE p.text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"James Madison's influence on US constitutional clauses\")\n LIMIT 3\n),\nImageMatches AS (\n SELECT i.image_id, i.article_id, i.filename, i.description, i.distance AS image_distance\n FROM Images i\n WHERE i.description_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"Portraits of significant US political figures\")\n LIMIT 3\n)\nSELECT am.article_id, am.title, am.url\nFROM ArticleMatches am\nJOIN ParagraphMatches pm ON am.article_id = pm.article_id\nJOIN ImageMatches im ON am.article_id = im.article_id\nORDER BY am.article_distance + pm.paragraph_distance + im.image_distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you identify the top article related to \"Historical legal procedures in the US government,\" which also contains a paragraph about \"James Madison's influence on US constitutional clauses\" and features an image of \"Portraits of significant US political figures\"? I need the article's title and URL.", + "external_knowledge": "", + "sql_candidate": [ + "WITH ArticleMatches AS (\n SELECT a.article_id, a.title, a.url, a.distance AS article_distance\n FROM Articles a\n WHERE a.raw_html_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"Historical legal procedures in the US government\")\n LIMIT 3\n),\nParagraphMatches AS (\n SELECT p.paragraph_id, p.article_id, p.text, p.distance AS paragraph_distance\n FROM Paragraphs p\n WHERE p.text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"James Madison's influence on US constitutional clauses\")\n LIMIT 3\n),\nImageMatches AS (\n SELECT i.image_id, i.article_id, i.filename, i.description, i.distance AS image_distance\n FROM Images i\n WHERE i.description_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \"Portraits of significant US political figures\")\n LIMIT 3\n)\nSELECT am.article_id, am.title, am.url\nFROM ArticleMatches am\nJOIN ParagraphMatches pm ON am.article_id = pm.article_id\nJOIN ImageMatches im ON am.article_id = im.article_id\nORDER BY am.article_distance + pm.paragraph_distance + im.image_distance\nLIMIT 1;" + ], + "execution_status": "exception", + "error_message": "约束缺失: 在表 'a' 上的向量搜索缺少 'k=N' 或 'LIMIT N' 约束。", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A mechanism by which the President of the United States appoints a current or former member of Congress to a civil office position.') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications on appointments by the US President.') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title, a.url, a.distance\n FROM a_filtered AS a\n ORDER BY a.distance\n),\n\nRelatedParagraphs AS (\n SELECT p.paragraph_id, p.article_id, p.text, p.distance\n FROM p_filtered AS p\n JOIN SimilarArticles sa ON toString(p.article_id) = toString(sa.article_id)\n WHERE its implications on appointments by the US President.') ORDER BY p.distance\n)\n\nSELECT sa.title, rp.text\nFROM SimilarArticles sa\nJOIN RelatedParagraphs rp ON toString(sa.article_id) = toString(rp.article_id)\nORDER BY rp.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Can you find some top articles and their related sections that discuss methods the US President uses to appoint Congress members, especially touching on the Saxbe fix?", + "external_knowledge": "The `MATCH` operator performs vector similarity searches using approximate nearest neighbor algorithms, which quickly find the top N items most similar to a provided concept based on vector embeddings. In this query, vectors from the model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' evaluate similarity, with smaller distances indicating higher similarity. The `k=5` specifies that the top 5 most similar results should be returned. This technique is commonly used in natural language processing to match semantic content effectively.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A mechanism by which the President of the United States appoints a current or former member of Congress to a civil office position.') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications on appointments by the US President.') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title, a.url, a.distance\n FROM a_filtered AS a\n ORDER BY a.distance\n),\n\nRelatedParagraphs AS (\n SELECT p.paragraph_id, p.article_id, p.text, p.distance\n FROM p_filtered AS p\n JOIN SimilarArticles sa ON toString(p.article_id) = toString(sa.article_id)\n WHERE its implications on appointments by the US President.') ORDER BY p.distance\n)\n\nSELECT sa.title, rp.text\nFROM SimilarArticles sa\nJOIN RelatedParagraphs rp ON toString(sa.article_id) = toString(rp.article_id)\nORDER BY rp.distance\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 22120 ('MATCH') (line 20, col 26): MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous political figure') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A discussion on legislative procedures') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of historical events') AS ref_vec_2,\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_2) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT i.url\nFROM i_filtered AS i\nJOIN a_filtered AS a ON toString(i.article_id) = toString(a.article_id)\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY i.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Retrieve the URL of the image that best exemplifies a famous political figure, associated with an article on legislative procedures and a paragraph providing a detailed analysis of historical events, ensuring the selection is based on the highest relevance across these topics.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous political figure') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A discussion on legislative procedures') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of historical events') AS ref_vec_2,\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_2) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT i.url\nFROM i_filtered AS i\nJOIN a_filtered AS a ON toString(i.article_id) = toString(a.article_id)\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY i.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Sixtus Muskie, U.S. Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the image that best captures the essence of Edmund Sixtus Muskie as the U.S. Secretary of State? I just need the image ID, please.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Sixtus Muskie, U.S. Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Muskie, a prominent political figure in US history, served as Secretary of State.') AS ref_vec_0\n\nSELECT a.title, i.description, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 53, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the titles and image descriptions for the top 5 articles that most pertinently cover the topic of Edmund Muskie, a significant political figure who served as Secretary of State in US history?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Muskie, a prominent political figure in US history, served as Secretary of State.') AS ref_vec_0\n\nSELECT a.title, i.description, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Modern architecture style') AS ref_vec_0\n\nSELECT a.article_id, a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the articles, including their IDs and titles, that have paragraphs best representing the Modern architecture style, considering the top 5 most relevant paragraphs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Modern architecture style') AS ref_vec_0\n\nSELECT a.article_id, a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative green building designs in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Reveal the titles of articles that soar through the skyline of innovation, focusing on groundbreaking green building designs in the Windy City.", + "external_knowledge": "- The `MATCH` operator is used for performing an approximate nearest neighbor (ANN) search, which identifies the most similar items based on vector representation.\n- The `lembed` function leverages a pre-trained vector model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`) to encode textual content into a high-dimensional space, allowing for semantic similarity evaluation.\n- The parameter `p.k = 1` indicates that the query aims to find the single most relevant paragraph that aligns closely with the specified concept.\n- In vector operations, similarity is typically assessed via Euclidean distance (L2 norm), with smaller distances indicating greater similarity.\n- \"Innovative green building designs in Chicago\" refers to architectural advancements that prioritize sustainability and ecological considerations in Chicago.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative green building designs in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the United States Constitution') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Articles_info ai ON toString(a.article_id) = toString(ai.key)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you dig up the titles of the top 5 articles that are all about \"The Saxbe fix\" and how it ties in with the United States Constitution? Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the United States Constitution') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Articles_info ai ON toString(a.article_id) = toString(ai.key)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table wikipedia_multimodal.Articles_info does not exist. Maybe you meant ai_and_technology_news_aggregation_and_analysis.ARTICLE_TAGS?. (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events and figures') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of significant historical figures') AS ref_vec_1,\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n WHERE heading_text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events\n ORDER BY distance\n LIMIT 10\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT a.title\nFROM Articles a\nJOIN h_filtered AS h ON toString(a.article_id) = toString(h.heading_id)\nJOIN i_filtered AS i ON toString(a.article_id) = toString(i.article_id)\n WHERE figures') ORDER BY h.distance + i.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "What is the title of the article that most closely relates to both historical events and figures, and includes portraits of significant historical figures?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events and figures') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of significant historical figures') AS ref_vec_1,\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n WHERE heading_text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events\n ORDER BY distance\n LIMIT 10\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 10\n)\n\nSELECT a.title\nFROM Articles a\nJOIN h_filtered AS h ON toString(a.article_id) = toString(h.heading_id)\nJOIN i_filtered AS i ON toString(a.article_id) = toString(i.article_id)\n WHERE figures') ORDER BY h.distance + i.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 21904 ('(') (line 5, col 15): (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n WHERE heading_text_embedding MATCH lembed('laion/C. Unmatched parentheses: (. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy and provide access to parking') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar power in buildings and support for sustainable design') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n WHERE raw_wikitext_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed(''laion/CLIP-ViT-B-32-laion2B-s34B-b79K'', ''solar power in buildings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\n WHERE provide access to parking') AND support for sustainable design') ORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the title of an article that talks about buildings powered by solar energy and offering parking, and also includes paragraphs about solar power and sustainable design? I just need the top choice, pretty please!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy and provide access to parking') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar power in buildings and support for sustainable design') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n WHERE raw_wikitext_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed(''laion/CLIP-ViT-B-32-laion2B-s34B-b79K'', ''solar power in buildings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\n WHERE provide access to parking') AND support for sustainable design') ORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 22704 ('') ORDER BY p.distance\nLIMIT 1\n FORMAT Native') (line 29, col 70): ') ORDER BY p.distance\nLIMIT 1\n FORMAT Native. Single quoted string is not closed: '') ORDER BY p.distance\nLIMIT 1\n FORMAT Native'. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the US Constitution''''s Ineligibility Clause') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Could you find a few paragraphs that have something to do with how the Saxbe fix relates to the Constitution, and let me know their content?", + "external_knowledge": "The query utilizes vector search, which involves converting textual data into numerical vectors using embeddings. The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, identifying items whose vector representations are closest to a target vector. Here, the CLIP model transforms the description of the Saxbe fix into a vector, and the search finds paragraphs with vectors most similar to this. The `k=5` limits the search to the five nearest items, based on Euclidean distance, where lower distances indicate greater similarity. This method allows for semantic matching beyond simple keyword matches.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the US Constitution''''s Ineligibility Clause') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous historical figure delivering a speech at the United Nations') AS ref_vec_0\n\nSELECT image_id, url, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Uncover the trio of visual tales that capture the essence of a renowned historical luminary speaking at the global symposium of the United Nations.", + "external_knowledge": "In this context, the `MATCH` operator is utilized to perform an approximate nearest neighbor (ANN) search. The query seeks the top 3 images that are most semantically aligned with the input description vector \"A famous historical figure delivering a speech at the United Nations.\" The `lembed` function generates a vector representation using the `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` model, which is then used to compare against the existing description embeddings in the database. The similarity between vectors is determined based on the Euclidean distance, with closer distances indicating higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous historical figure delivering a speech at the United Nations') AS ref_vec_0\n\nSELECT image_id, url, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of historical events and impacts') AS ref_vec_0\n\nSELECT a.article_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%significant development%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you list the 5 articles that most relate to the analysis of historical events and impacts, and include a significant development in their paragraphs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of historical events and impacts') AS ref_vec_0\n\nSELECT a.article_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%significant development%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States government') AS ref_vec_0\n\nSELECT a.title, p.text, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index < 5\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 25, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to find the titles and content of the first five paragraphs from the top 5 articles that are most relevant to the \"History of the United States government\".", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States government') AS ref_vec_0\n\nSELECT a.title, p.text, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index < 5\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park and solar energy in Chicago') AS ref_vec_0,\n\nArticleWikitextCTE AS (\n SELECT a.article_id, a.title, a.url, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM ArticleWikitextCTE\nJOIN Paragraphs p ON toString(ArticleWikitextCTE.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%Chicago%'\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 5 articles related to Millennium Park and solar energy in Chicago, and among those, find one that includes a paragraph mentioning Chicago. What is the title of that article?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park and solar energy in Chicago') AS ref_vec_0,\n\nArticleWikitextCTE AS (\n SELECT a.article_id, a.title, a.url, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM ArticleWikitextCTE\nJOIN Paragraphs p ON toString(ArticleWikitextCTE.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%Chicago%'\nLIMIT 1;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional Convention and the Saxbe fix') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Could you provide the title of the article that is most relevant to the concept of \"Constitutional Convention and the Saxbe fix\"? Ensure that you find the top match based on similarity and return only one title.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional Convention and the Saxbe fix') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Deep analysis of constitutional law and its historical implications') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you identify the titles of articles and the calculated similarity distances for the top 5 paragraphs that pertain to an in-depth study of constitutional law and its historical impacts?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Deep analysis of constitutional law and its historical implications') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar power generation in buildings') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Articles_info ai ON toString(a.wiki_id) = toString(ai.key)\nWHERE ai.value LIKE '%environmental%'\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the titles of the top 5 articles that are highly relevant to the topic of solar power generation in buildings and have environmental aspects discussed. These articles should be selected from the top 10 most pertinent articles based on their content embedding similarity, and should be the closest in order of relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar power generation in buildings') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Articles_info ai ON toString(a.wiki_id) = toString(ai.key)\nWHERE ai.value LIKE '%environmental%'\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table wikipedia_multimodal.Articles_info does not exist. Maybe you meant ai_and_technology_news_aggregation_and_analysis.ARTICLE_TAGS?. (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A notable historical figure known for their diplomatic efforts during times of international tension') AS ref_vec_0\n\nSELECT a.title, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Articles a ON toString(i.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Find the top article title related to a notable historical figure known for diplomatic efforts during international tension.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A notable historical figure known for their diplomatic efforts during times of international tension') AS ref_vec_0\n\nSELECT a.title, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Articles a ON toString(i.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of modern architecture and green design in city structures') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few articles that delve into the modern and eco-friendly aspects of architecture within urban environments?", + "external_knowledge": "The \"MATCH\" operator in SQLite-vec performs an approximate nearest neighbor (ANN) search, allowing for efficient retrieval of items that are most similar to a given vector representation. The phrase \"modern architecture and green design in city structures\" is converted into a vector using the 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model, which captures semantic meaning and context. The query retrieves the top k=3 most similar items, using Euclidean distance as a measure of similarity. In practical terms, this query is identifying articles that are most related to themes of contemporary architecture and sustainable design in urban settings.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of modern architecture and green design in city structures') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring the constitutional dynamics and historical context of the Saxbe fix mechanism') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 paragraph IDs where the text dives into the constitutional dynamics and history around the Saxbe fix mechanism?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring the constitutional dynamics and historical context of the Saxbe fix mechanism') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The influence of historical events on modern culture and society') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title\nFROM \n Articles a\nJOIN \n RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nWHERE \n a.title LIKE '%History%'\nORDER BY \n rp.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 5 articles related to the influence of historical events on modern culture and society, focusing on those with \"History\" in their titles?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The influence of historical events on modern culture and society') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title\nFROM \n Articles a\nJOIN \n RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nWHERE \n a.title LIKE '%History%'\nORDER BY \n rp.distance;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix is a legislative strategy to address the Ineligibility Clause') AS ref_vec_0,\n\nSimilarHeadings AS (\n SELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT heading_text\nFROM SimilarHeadings;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the top 5 headings related to the legislative strategy known as the Saxbe fix used to address the Ineligibility Clause?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix is a legislative strategy to address the Ineligibility Clause') AS ref_vec_0,\n\nSimilarHeadings AS (\n SELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT heading_text\nFROM SimilarHeadings;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed explanation about the Saxbe fix and its implications.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the IDs and similarity distances for the top 5 paragraphs that most effectively explain the Saxbe fix and its implications?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed explanation about the Saxbe fix and its implications.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The impact of technology on modern education systems') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced technological devices in classrooms') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarParagraphs AS (\n SELECT\n p.paragraph_id AS paragraph_id,\n p.article_id AS article_id,\n p.paragraph_index AS paragraph_index,\n p.text AS text,\n p.distance AS paragraph_distance\n FROM p_filtered AS p\n ORDER BY\n paragraph_distance\n),\n\nRelatedImages AS (\n SELECT\n i.image_id AS image_id,\n i.article_id AS article_id,\n i.filename AS filename,\n i.image_title AS image_title,\n i.url AS url,\n i.distance AS image_distance\n FROM i_filtered AS i\n ORDER BY\n image_distance\n)\n\nSELECT\n sp.article_id AS article_id\nFROM\n SimilarParagraphs sp\nJOIN\n RelatedImages ri ON toString(sp.article_id) = toString(ri.article_id)\nORDER BY\n sp.paragraph_distance + ri.image_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which article stands out for its insights on how technology reshapes classrooms and includes visuals of cutting-edge educational devices?", + "external_knowledge": "In this context, the vector operations involve the `MATCH` function, which performs an approximate nearest neighbor (ANN) search to find the closest matches for specified concepts. The `k=5` indicates the top 5 results are considered based on their vector proximity, determined by Euclidean distance. The embeddings used reflect the semantic meaning of phrases related to the impact of technology on education and advanced devices in classrooms, implying that paragraphs and images are selected based on how well they align with these themes.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The impact of technology on modern education systems') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced technological devices in classrooms') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarParagraphs AS (\n SELECT\n p.paragraph_id AS paragraph_id,\n p.article_id AS article_id,\n p.paragraph_index AS paragraph_index,\n p.text AS text,\n p.distance AS paragraph_distance\n FROM p_filtered AS p\n ORDER BY\n paragraph_distance\n),\n\nRelatedImages AS (\n SELECT\n i.image_id AS image_id,\n i.article_id AS article_id,\n i.filename AS filename,\n i.image_title AS image_title,\n i.url AS url,\n i.distance AS image_distance\n FROM i_filtered AS i\n ORDER BY\n image_distance\n)\n\nSELECT\n sp.article_id AS article_id\nFROM\n SimilarParagraphs sp\nJOIN\n RelatedImages ri ON toString(sp.article_id) = toString(ri.article_id)\nORDER BY\n sp.paragraph_distance + ri.image_distance\nLIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and electricity generation in buildings') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticlesWithImages AS (\n SELECT a.article_id, a.title, i.image_id, i.filename, i.description\n FROM Articles a\n JOIN Images i ON toString(a.article_id) = toString(i.article_id)\n)\n\nSELECT a.title\nFROM ArticlesWithImages a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top article related to solar energy and electricity generation in buildings, which also includes images, and give me its title.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and electricity generation in buildings') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticlesWithImages AS (\n SELECT a.article_id, a.title, i.image_id, i.filename, i.description\n FROM Articles a\n JOIN Images i ON toString(a.article_id) = toString(i.article_id)\n)\n\nSELECT a.title\nFROM ArticlesWithImages a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'an explanation about legislative mechanisms in the US') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM VectorSearchResults\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "What are the paragraph IDs for the top 5 paragraphs explaining legislative mechanisms in the US?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'an explanation about legislative mechanisms in the US') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM VectorSearchResults\nORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of solar energy generation in urban architecture') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id,\n a.title AS title,\n a.url AS url,\n p.text AS text,\n distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Which five literary lanterns illuminate the synergy between solar power and cityscape creation within the architectural cosmos, shining through in articles with their titles and digital paths?", + "external_knowledge": "In this query, the `MATCH` operator is used to perform an approximate nearest neighbor search, comparing the text embeddings of paragraphs to the vector representation of the given query using a model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`). The `k = 5` specifies that we want the top 5 most relevant instances. Vectors, which represent semantic meaning, are compared using Euclidean distance; the lower the distance, the more similar the content. The query thus extracts paragraphs closely aligned with the thematic essence of solar energy within urban architecture.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of solar energy generation in urban architecture') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id,\n a.title AS title,\n a.url AS url,\n p.text AS text,\n distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles that are all about solar energy and architecture in Chicago? I'm just looking for their titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical InformationArticle content about history') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical Events') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\nHeadings_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_1) AS distance\n FROM Headings\n WHERE parent_heading_id IN (\n SELECT article_id FROM RelevantArticles\n)\n ORDER BY distance\n LIMIT 5\n ),\n\nRelevantArticles AS (\n SELECT article_id\n FROM Articles_filtered AS Articles\n)\n\nSELECT heading_text\nFROM Headings_filtered AS Headings;", + "sql_result_column_count": 1, + "sql_result_rows_count": 4, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "**\n\nCan you show me the top 5 headings that are most relevant to historical events, and are part of articles about history?\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical InformationArticle content about history') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical Events') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\nHeadings_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_1) AS distance\n FROM Headings\n WHERE parent_heading_id IN (\n SELECT article_id FROM RelevantArticles\n)\n ORDER BY distance\n LIMIT 5\n ),\n\nRelevantArticles AS (\n SELECT article_id\n FROM Articles_filtered AS Articles\n)\n\nSELECT heading_text\nFROM Headings_filtered AS Headings;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table wikipedia_multimodal.RelevantArticles does not exist: While processing parent_heading_id IN ((WITH [-0.33587026596069336, 0.44062888622283936, -0.13216087222099304, -0.0676916316151619, 0.06583104282617569, -0.03458724915981293, 0.02470780909061432, 0.2597261369228363, 0.13574287295341492, 0.10980917513370514, 0.051403507590293884, 0.45708590745925903, -0.1417456716299057, 0.23655655980110168, 0.26098471879959106, 0.16768135130405426, 0.17870797216892242, 0.13221219182014465, 0.1396690011024475, -0.06668780744075775, -0.006523787975311279, 0.09262089431285858, 0.06550203263759613, 0.04137711971998215, -0.22346502542495728, -0.11826446652412415, 0.010911300778388977, 0.39458033442497253, 0.07037353515625, 0.31510597467422485, -0.13363447785377502, -0.08950287848711014, 0.004515163600444794, 0.12881726026535034, 0.16660158336162567, 0.026072070002555847, 0.1562112271785736, 0.13681401312351227, 0.13827723264694214, -0.008661588653922081, -0.19861432909965515, 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-0.006473541259765625, 0.10025617480278015, 0.056051626801490784, 0.14834529161453247, 0.3929353952407837, 0.5009462237358093, 0.45056700706481934, 0.5098791122436523, 0.09516757726669312, 0.12857714295387268, 0.043599456548690796, 0.11125046014785767, 0.04472946748137474, -0.046812430024147034, -0.02020224928855896, 0.07082003355026245, -0.018338531255722046, -0.16343989968299866, 0.3005440831184387, -0.123715840280056, -0.1851714849472046, 0.048045434057712555, 0.19383639097213745, -0.06194598972797394, -0.20305103063583374, -0.749584436416626, 0.09169220924377441, 0.1512603908777237, 0.05776897072792053, -0.0640963613986969, -0.08527320623397827, -0.08563154935836792, 0.2958425283432007, 0.12752455472946167, 0.21495334804058075, 0.1335921585559845, -0.29536619782447815, -0.040219128131866455, -0.11741279065608978, 0.011067911982536316, -0.014095991849899292, -0.0831901878118515, -0.015787623822689056, -0.4268133044242859, -0.4310533404350281, -0.12585973739624023, 0.19305887818336487, 0.17000047862529755, 0.12639495730400085, 0.14936107397079468, 0.08482187241315842, -0.13271664083003998, 0.057266175746917725, 0.10956704616546631, -0.018925562500953674, 0.09550417214632034] AS ref_vec_1 SELECT article_id FROM RelevantArticles) AS _subquery20). (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you find the article that best represents the topic of solar energy and architecture in Chicago? I need the article's ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'significant historical events and figures of the 20th century') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, img.filename\nFROM SimilarArticles sa\nJOIN Images img ON toString(sa.article_id) = toString(img.article_id)\nWHERE img.is_icon = 'No'\nAND img.k = 5\nORDER BY img.image_id;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the top five articles related to significant historical events and figures of the 20th century, along with their associated non-icon images where the value of `k` is 5, and provide the image filenames ordered by image ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'significant historical events and figures of the 20th century') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT sa.title, img.filename\nFROM SimilarArticles sa\nJOIN Images img ON toString(sa.article_id) = toString(img.article_id)\nWHERE img.is_icon = 'No'\nAND img.k = 5\nORDER BY img.image_id;" + ], + "integration_level": 3, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There's no column 'img.k' in table 'img': While processing img.k: While processing WITH [-0.050596579909324646, 0.16515211760997772, -0.2635791599750519, 0.3090002238750458, -0.384818434715271, 0.011387763544917107, -0.2709793746471405, -0.6655935645103455, -0.1053910180926323, -0.10532136261463165, 0.19827862083911896, 0.15201103687286377, 0.2031172811985016, -0.2830464243888855, 0.3385624289512634, 0.130210280418396, -0.0656764805316925, 0.04288192093372345, -0.1244107037782669, 0.22873397171497345, 0.18798145651817322, 0.009661972522735596, 0.06429529935121536, -0.2289467453956604, 0.1296275109052658, 0.06153133511543274, 0.19673322141170502, 0.02379167452454567, 0.15541194379329681, 0.15178237855434418, 0.21391797065734863, -0.2456573247909546, 0.013719577342271805, 0.17039185762405396, -0.15502992272377014, 0.09247371554374695, -0.10403484851121902, -0.19806671142578125, 0.16281884908676147, 0.21691781282424927, -0.035017456859350204, 0.22454090416431427, 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-0.20135176181793213, -0.03360696882009506, 0.06391796469688416, 0.02304895967245102, 0.2779390811920166, 0.38823258876800537, 0.18170690536499023, 0.22613321244716644, -0.06101090461015701, -0.19805599749088287, -0.1094479113817215, -0.2645413875579834, 0.05319274589419365, -0.06932482868432999, -0.09693735092878342, -0.00694243423640728, 0.38238465785980225, -0.2991320490837097, 0.37184903025627136, -0.17406058311462402, -0.10418323427438736, 0.05134325847029686, 0.244792178273201, -0.03735152259469032, -0.2403612732887268, -1.0991110801696777, -0.020114649087190628, 0.09482792019844055, -0.1463586390018463, -0.058228448033332825, -0.20961253345012665, 0.04148264229297638, 0.09453994780778885, 0.48803260922431946, 0.19429878890514374, 0.10525878518819809, -0.0867370143532753, -0.3377227187156677, 0.03137646242976189, 0.15837767720222473, -0.05455248802900314, -0.13334062695503235, 0.06370200216770172, -0.18692168593406677, -0.28546229004859924, 0.25436022877693176, 0.1686478555202484, -0.010789044201374054, -0.026463322341442108, 0.22701039910316467, -0.005385430529713631, -0.009072378277778625, 0.20956452190876007, 0.3131084442138672, 0.2077106088399887, 0.1203153058886528] AS ref_vec_0 SELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance FROM Articles ORDER BY distance ASC LIMIT 5) SELECT sa.title, img.filename FROM SimilarArticles AS sa INNER JOIN Images AS img ON toString(sa.article_id) = toString(img.article_id) WHERE (is_icon = 'No') AND (img.k = 5) ORDER BY img.image_id ASC. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has subsequently become relevant for appointments by presidents of both parties') AS ref_vec_0,\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticleDetails AS (\n SELECT article_id, url\n FROM Articles\n)\n\nSELECT ad.url\nFROM ParagraphMatches pm\nJOIN ArticleDetails ad ON toString(pm.article_id) = toString(ad.article_id)\nORDER BY pm.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the URL of the most relevant article that discusses the Saxbe fix's importance in presidential appointments?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has subsequently become relevant for appointments by presidents of both parties') AS ref_vec_0,\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticleDetails AS (\n SELECT article_id, url\n FROM Articles\n)\n\nSELECT ad.url\nFROM ParagraphMatches pm\nJOIN ArticleDetails ad ON toString(pm.article_id) = toString(ad.article_id)\nORDER BY pm.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy in modern architecture') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Top 5 articles on generating electricity from solar energy in modern architecture. Return their IDs and titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy in modern architecture') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions.') AS ref_vec_0,\n\ntop_paragraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, tp.distance\nFROM top_paragraphs tp\nJOIN Articles a ON toString(tp.article_id) = toString(a.article_id)\nORDER BY tp.distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find the article that has a paragraph most relevant to the idea of \"The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions\"? I'd love to know the title and URL of the article that comes out on top!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions.') AS ref_vec_0,\n\ntop_paragraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, tp.distance\nFROM top_paragraphs tp\nJOIN Articles a ON toString(tp.article_id) = toString(a.article_id)\nORDER BY tp.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed exploration of a historical event') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the impact') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nArticleMatches AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.text\nFROM ArticleMatches am\nJOIN p_filtered AS p ON toString(am.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "(Natural Language Question capturing all query elements)", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed exploration of a historical event') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the impact') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nArticleMatches AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.text\nFROM ArticleMatches am\nJOIN p_filtered AS p ON toString(am.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Important political appointment by President Carter') AS ref_vec_0\n\nSELECT i.url, distance(i.caption_embedding, ref_vec_0) AS distance \nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 7, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide the URLs for the top 3 images related to the significant political appointment made by President Carter?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Important political appointment by President Carter') AS ref_vec_0\n\nSELECT i.url, distance(i.caption_embedding, ref_vec_0) AS distance \nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'caption_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of modern technology trends') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An image depicting advanced technology devices') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technology advancements') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_2) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title, a.url, distance \n FROM a_filtered AS a\n),\n\nSimilarImages AS (\n SELECT i.image_id, i.filename, i.url, i.article_id, distance\n FROM i_filtered AS i\n),\n\nRelatedHeadings AS (\n SELECT h.heading_id, h.heading_text\n FROM h_filtered AS h\n)\n\nSELECT sa.title\nFROM SimilarArticles sa\nJOIN SimilarImages si ON toString(sa.article_id) = toString(si.article_id)\nJOIN Image_Headings ih ON toString(si.image_id) = toString(ih.image_id)\nJOIN RelatedHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY sa.distance, si.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you please locate the article title that best aligns with a detailed study on modern technology trends, particularly those articles featuring images of advanced technology devices and related to headings on technology advancements? Just give me the top one, please!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of modern technology trends') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An image depicting advanced technology devices') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technology advancements') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_2) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title, a.url, distance \n FROM a_filtered AS a\n),\n\nSimilarImages AS (\n SELECT i.image_id, i.filename, i.url, i.article_id, distance\n FROM i_filtered AS i\n),\n\nRelatedHeadings AS (\n SELECT h.heading_id, h.heading_text\n FROM h_filtered AS h\n)\n\nSELECT sa.title\nFROM SimilarArticles sa\nJOIN SimilarImages si ON toString(sa.article_id) = toString(si.article_id)\nJOIN Image_Headings ih ON toString(si.image_id) = toString(ih.image_id)\nJOIN RelatedHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY sa.distance, si.distance\nLIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States constitutional law') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Political terminology of the United States') AS ref_vec_1,\n\na1_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\na2_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a1.title AS Article_Title, \n a2.title AS Related_Article_Title\nFROM a1_filtered AS a1\nJOIN a2_filtered AS a2 ON a1.article_id != a2.article_id \nORDER BY \n a1.article_id, a2.article_id;", + "sql_result_column_count": 2, + "sql_result_rows_count": 21, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify and list the titles of articles that are among the top 5 most relevant to \"United States constitutional law\", along with the titles of related articles that are among the top 5 most relevant to \"Political terminology of the United States\", ensuring that no article is paired with itself. Order the results by the article IDs of both sets.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States constitutional law') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Political terminology of the United States') AS ref_vec_1,\n\na1_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\na2_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a1.title AS Article_Title, \n a2.title AS Related_Article_Title\nFROM a1_filtered AS a1\nJOIN a2_filtered AS a2 ON a1.article_id != a2.article_id \nORDER BY \n a1.article_id, a2.article_id;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 403, server response: Code: 403. DB::Exception: Unsupported JOIN ON conditions. Unexpected 'article_id != a2.article_id': While processing article_id != a2.article_id. (INVALID_JOIN_ON_EXPRESSION) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH ArticleMatches AS (\n SELECT \n a.article_id AS article_id,\n a.title AS title, \n a.distance AS article_distance\n FROM Articles a\n WHERE a.raw_wikitext_embedding MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \n 'Historical events in early 20th century'\n ) \n AND a.k = 5\n),\nParagraphMatches AS (\n SELECT \n p.paragraph_id AS paragraph_id,\n p.article_id AS article_id,\n p.text AS text,\n p.distance AS paragraph_distance\n FROM Paragraphs p\n WHERE p.text_embedding MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K',\n 'Detailed analysis of global impacts'\n ) \n AND p.k = 5\n),\nImageMatches AS (\n SELECT \n i.image_id AS image_id,\n i.article_id AS article_id,\n i.filename AS filename,\n i.caption AS caption,\n i.distance AS image_distance\n FROM Images i\n WHERE i.description_embedding MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \n 'Iconic photographs from history'\n ) \n AND i.k = 5\n)\nSELECT \n am.title AS article_title,\n pm.text AS paragraph_text\nFROM ArticleMatches am\nJOIN ParagraphMatches pm ON toString(am.article_id) = toString(pm.article_id)\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "Could you provide the titles of articles and the texts of paragraphs that discuss historical events in the early 20th century and offer a detailed analysis of global impacts, selecting the top 5 articles and the top 5 paragraphs, with each paragraph belonging to its respective article? Limit the results to a total of 10 entries.", + "external_knowledge": "", + "sql_candidate": [ + "WITH ArticleMatches AS (\n SELECT \n a.article_id AS article_id,\n a.title AS title, \n a.distance AS article_distance\n FROM Articles a\n WHERE a.raw_wikitext_embedding MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \n 'Historical events in early 20th century'\n ) \n AND a.k = 5\n),\nParagraphMatches AS (\n SELECT \n p.paragraph_id AS paragraph_id,\n p.article_id AS article_id,\n p.text AS text,\n p.distance AS paragraph_distance\n FROM Paragraphs p\n WHERE p.text_embedding MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K',\n 'Detailed analysis of global impacts'\n ) \n AND p.k = 5\n),\nImageMatches AS (\n SELECT \n i.image_id AS image_id,\n i.article_id AS article_id,\n i.filename AS filename,\n i.caption AS caption,\n i.distance AS image_distance\n FROM Images i\n WHERE i.description_embedding MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \n 'Iconic photographs from history'\n ) \n AND i.k = 5\n)\nSELECT \n am.title AS article_title,\n pm.text AS paragraph_text\nFROM ArticleMatches am\nJOIN ParagraphMatches pm ON toString(am.article_id) = toString(pm.article_id)\nLIMIT 10;" + ], + "integration_level": 0, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 177 ('MATCH') (line 7, col 34): MATCH lembed(\n 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', \n 'Historical events in early 20th century'\n ) \n AND a.k = 5\n),\nParagraphMatches AS (\n SELECT \n. Expected one of: ParserArrayOfJSONIdentifierDelimiter, token sequence, OpeningSquareBracket, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and its impact on clean energy generation') AS ref_vec_0\n\nSELECT p.text, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 172, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Could you find a few paragraphs from the articles that discuss solar energy's role in promoting clean energy?", + "external_knowledge": "The `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search, which finds data points in a vector space that are closest to a given query vector. In this context, \"lembed\" refers to the embedding of a phrase using the specified model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'. The `k=5` parameter specifies that the query should return the top 5 articles most relevant to the topic of solar energy and its influence on clean energy generation. The similarity is measured using Euclidean distance (L2 norm), where a smaller distance implies higher similarity. This approach is frequently used in information retrieval to find text or documents related to a specific topic based on semantic similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and its impact on clean energy generation') AS ref_vec_0\n\nSELECT p.text, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President appointing ambassador and commission chair') AS ref_vec_0\n\nSELECT i.caption, h.heading_text, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Can you find the top 5 images that are related to the idea of \"President appointing ambassador and commission chair,\" and show me their captions along with the headings?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President appointing ambassador and commission chair') AS ref_vec_0\n\nSELECT i.caption, h.heading_text, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'caption_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of events and occurrences in a specific context') AS ref_vec_0\n\nSELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the heading that best describes the history of events and occurrences in a specific context?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of events and occurrences in a specific context') AS ref_vec_0\n\nSELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison proposed language at the Constitutional Convention that was adopted as the Ineligibility Clause after debate.') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url,\n distance(p.text_embedding, ref_vec_0) AS distance \nFROM \n Articles a\nJOIN \n Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Top 3 articles related to James Madison's proposal at the Constitutional Convention. List their IDs, titles, and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison proposed language at the Constitutional Convention that was adopted as the Ineligibility Clause after debate.') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url,\n distance(p.text_embedding, ref_vec_0) AS distance \nFROM \n Articles a\nJOIN \n Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix in U.S. politics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, p.text, a.title, a.url\nFROM Paragraphs p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY a.distance, p.paragraph_index\nLIMIT 10;", + "sql_result_column_count": 4, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to find paragraphs from articles that are topically relevant to \"The Saxbe fix in U.S. politics\". Please provide the IDs and text of the first 10 paragraphs from the top 5 articles, including the articles' titles and URLs. Ensure the paragraphs are sorted based on the articles' relevance and within the articles themselves.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix in U.S. politics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, p.text, a.title, a.url\nFROM Paragraphs p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY a.distance, p.paragraph_index\nLIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The United States Congress and its legislative mechanisms') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional Clauses related to legislative appointments') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Diplomatic positions and ambassadorial appointments') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The United States Congress\n ORDER BY distance\n LIMIT 3\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_2) AS distance\n FROM Images\n WHERE description_embedding MATCH lembed(''laion/CLIP-ViT-B-32-laion2B-s34B-b79K'', ''Diplomatic positions\n ORDER BY distance\n LIMIT 2\n)\n\nSELECT p.paragraph_id\nFROM p_filtered AS p\nJOIN a_filtered AS a ON toString(p.article_id) = toString(a.article_id)\nJOIN i_filtered AS i ON toString(a.article_id) = toString(i.article_id)\n WHERE its legislative mechanisms') AND ambassadorial appointments') ORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you find the paragraph ID that is part of an article associated with both the United States Congress and legislative mechanisms and constitutional clauses related to legislative appointments, which also includes images described by diplomatic and ambassadorial appointments? Please ensure that the returned paragraph is among the top 3 in terms of relevance to the Congress theme, top 5 for constitutional aspects, and top 2 for image descriptions, with the highest overall similarity.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The United States Congress and its legislative mechanisms') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional Clauses related to legislative appointments') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Diplomatic positions and ambassadorial appointments') AS ref_vec_2,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The United States Congress\n ORDER BY distance\n LIMIT 3\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_2) AS distance\n FROM Images\n WHERE description_embedding MATCH lembed(''laion/CLIP-ViT-B-32-laion2B-s34B-b79K'', ''Diplomatic positions\n ORDER BY distance\n LIMIT 2\n)\n\nSELECT p.paragraph_id\nFROM p_filtered AS p\nJOIN a_filtered AS a ON toString(p.article_id) = toString(a.article_id)\nJOIN i_filtered AS i ON toString(a.article_id) = toString(i.article_id)\n WHERE its legislative mechanisms') AND ambassadorial appointments') ORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 33649 ('') ORDER BY p.distance\nLIMIT 1\n FORMAT Native') (line 40, col 67): ') ORDER BY p.distance\nLIMIT 1\n FORMAT Native. Single quoted string is not closed: '') ORDER BY p.distance\nLIMIT 1\n FORMAT Native'. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy sources in urban architecture') AS ref_vec_0,\n\nParagraphsMatch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS paragraph_distance\n FROM Paragraphs\n ORDER BY paragraph_distance\n LIMIT 5\n),\n\nRelatedArticles AS (\n SELECT a.article_id, a.title, a.url, pm.paragraph_distance\n FROM Articles a\n JOIN ParagraphsMatch pm ON toString(a.article_id) = toString(pm.article_id)\n),\n\nRelatedImages AS (\n SELECT i.image_id, i.url, ih.heading_id, h.heading_text, a.title AS article_title\n FROM Images i\n JOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\n JOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\n JOIN RelatedArticles a ON a.title LIKE '%' || h.heading_text || '%'\n)\n\nSELECT ri.url AS image_url\nFROM RelatedImages ri\nORDER BY ri.heading_id, ri.article_title\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 10, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you provide the URLs of the top 10 images linked to articles concerning renewable energy sources in urban architecture? The images should be sorted first by their heading ID and then by the article title to ensure relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy sources in urban architecture') AS ref_vec_0,\n\nParagraphsMatch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS paragraph_distance\n FROM Paragraphs\n ORDER BY paragraph_distance\n LIMIT 5\n),\n\nRelatedArticles AS (\n SELECT a.article_id, a.title, a.url, pm.paragraph_distance\n FROM Articles a\n JOIN ParagraphsMatch pm ON toString(a.article_id) = toString(pm.article_id)\n),\n\nRelatedImages AS (\n SELECT i.image_id, i.url, ih.heading_id, h.heading_text, a.title AS article_title\n FROM Images i\n JOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\n JOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\n JOIN RelatedArticles a ON a.title LIKE '%' || h.heading_text || '%'\n)\n\nSELECT ri.url AS image_url\nFROM RelatedImages ri\nORDER BY ri.heading_id, ri.article_title\nLIMIT 10;" + ], + "integration_level": 1, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 403, server response: Code: 403. DB::Exception: Unsupported JOIN ON conditions. Unexpected 'title LIKE concat('%', heading_text, '%')': While processing title LIKE concat('%', heading_text, '%'). (INVALID_JOIN_ON_EXPRESSION) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History') AS ref_vec_0\n\nSELECT a.title, distance(h.heading_text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 3 articles that are most related to the topic of History based on their headings?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History') AS ref_vec_0\n\nSELECT a.title, distance(h.heading_text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the evolution of modern architecture across the globe') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you find the paragraph that best captures the theme of evolving modern architecture worldwide and share its content with me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the evolution of modern architecture across the globe') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The impact of renewable energy on urban architecture.') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n p.text AS text, \n distance(p.text_embedding, ref_vec_0) AS paragraph_distance\nFROM \n Articles a\nJOIN \n Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE \n a.wiki_id IN (\n SELECT wiki_id \n FROM Articles_info \n WHERE value LIKE '%green architecture%'\n )\nORDER BY paragraph_distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey, can you list the top 10 articles about green architecture and their paragraphs that are most related to how renewable energy affects urban building designs? I want to see the article IDs, their titles, and the paragraph details!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The impact of renewable energy on urban architecture.') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n p.text AS text, \n distance(p.text_embedding, ref_vec_0) AS paragraph_distance\nFROM \n Articles a\nJOIN \n Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE \n a.wiki_id IN (\n SELECT wiki_id \n FROM Articles_info \n WHERE value LIKE '%green architecture%'\n )\nORDER BY paragraph_distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table wikipedia_multimodal.Articles_info does not exist. Maybe you meant ai_and_technology_news_aggregation_and_analysis.ARTICLE_TAGS?: While processing wiki_id IN ((WITH [-0.0430372953414917, 0.1421070694923401, -0.013189977034926414, -0.26412543654441833, 0.062696672976017, 0.09110061079263687, -0.042974554002285004, -0.5360222458839417, 0.3519155979156494, 0.029904630035161972, 0.1233893483877182, 0.06545376777648926, -0.1845545768737793, -0.21633966267108917, -0.21533600986003876, -0.08909538388252258, -0.5707907676696777, -0.08212796598672867, -0.10867784172296524, 0.2290794849395752, 0.5889421105384827, -0.11545462161302567, 0.307207852602005, 0.6279879808425903, -0.03399459645152092, -0.07401914149522781, -0.060208652168512344, 0.03574036434292793, -0.025956757366657257, 0.41054871678352356, -0.023302141577005386, -0.36949896812438965, -0.2846843898296356, -0.035691313445568085, 0.29524409770965576, 0.19721022248268127, 0.07397627830505371, 0.20293781161308289, -0.20604078471660614, -0.008717802353203297, 0.02181277982890606, 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-0.06590620428323746, 0.29753565788269043, 0.2791396379470825, -0.056702058762311935, 0.17750583589076996, -0.09217345714569092, -0.08617829531431198, 0.0937943160533905, 0.1825340986251831, 0.14565035700798035, -0.3294702172279358, -0.0356840044260025, 0.3051479160785675, -0.08498962223529816, -0.020174911245703697, -0.4324145019054413, 0.2185857594013214, -0.2630993127822876, -0.3427891135215759, 0.0625433698296547, -0.1157044842839241, 0.018131129443645477, 0.08299432694911957, 0.4493676424026489, 0.00397544726729393, -0.08712716400623322, 0.11564113944768906, -0.1923673152923584, 0.024959392845630646, -0.3657773435115814, 0.1067061498761177, -0.07603730261325836, -0.33766037225723267, 0.07531622052192688, 0.1739892214536667, 0.03713429346680641, 0.12718363106250763, -0.20692747831344604, -0.2179393768310547, -0.2946588397026062, -0.2191036194562912, 0.13982905447483063, -0.24052409827709198, 0.4585666060447693, -0.3029485046863556, -0.03255017101764679, -0.017128078266978264, 0.11443156003952026, 0.7271337509155273, 0.1812688708305359, -0.22137822210788727] AS ref_vec_0 SELECT wiki_id FROM Articles_info WHERE value LIKE '%green architecture%') AS _subquery23). (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A historical image of an important political figure.') AS ref_vec_0\n\nSELECT image_id, description, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 images that showcase historical figures in politics, and tell me their descriptions?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A historical image of an important political figure.') AS ref_vec_0\n\nSELECT image_id, description, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Key highlights and updates on modern architecture in urban spaces.') AS ref_vec_0\n\nSELECT article_id, wiki_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 articles that provide key highlights and updates on modern architecture in urban spaces? I need to know their IDs, titles, and where I can access them.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Key highlights and updates on modern architecture in urban spaces.') AS ref_vec_0\n\nSELECT article_id, wiki_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion of legislative branch mechanisms exemplified by the Saxbe fix') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID and similarity distance of the top article related to the discussion of legislative branch mechanisms, specifically the Saxbe fix?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion of legislative branch mechanisms exemplified by the Saxbe fix') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States Senate') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the paragraph IDs, article IDs, their index positions, and the paragraph text for the top 5 paragraphs that are most related to the \"History of the United States Senate\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States Senate') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The significance of the Saxbe fix in U.S. appointments and its constitutional implications') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "What’s one notable piece about how the Saxbe fix impacts U.S. appointments and its constitutional aspects?", + "external_knowledge": "The \"MATCH\" operator in this context is used to perform an approximate nearest neighbor (ANN) search, which is a type of vector search that identifies items most similar to a given query based on vector embeddings. The function 'lembed' is part of the sqlite-vec extension and is used to generate these vector embeddings from the specified text model ('laion/CLIP-ViT-B-32-laion2B-s34B-b79K'). The vector search ranks entries by how closely they match the input concept, limiting the results to the top match ('LIMIT 1'). The use of vector embeddings allows for semantic similarity comparisons, meaning paragraphs are evaluated not just on keyword matching but on overall thematic and contextual similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The significance of the Saxbe fix in U.S. appointments and its constitutional implications') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix relates to legislative actions and appointments.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT paragraph_id\nFROM SimilarParagraphs;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "What is the paragraph ID for the paragraph most related to \"The Saxbe fix relates to legislative actions and appointments\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix relates to legislative actions and appointments.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT paragraph_id\nFROM SimilarParagraphs;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Sustainable energy buildings in urban environments') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the bustling cityscape where innovation meets ecology, which are the three leading articles illuminating the path of sustainable energy buildings nestled within urban jungles? Please uncover their titles and doorways to enlightenment.", + "external_knowledge": "The `MATCH` operator in vector searches performs an approximate nearest neighbor (ANN) search to find items most similar to a given concept. The `lembed` function evaluates embeddings, which are vector representations of concepts, against stored data. In this instance, \"Sustainable energy buildings in urban environments\" is the concept being explored using embeddings from the `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` model, which is trained to understand visual and textual content semantically. The query asks for `k=3`, meaning it retrieves the top three articles with the smallest Euclidean distance (L2 norm) from the search concept, indicating the highest similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Sustainable energy buildings in urban environments') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Wikipedia article about legislative processes') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url, \n i.filename AS filename, \n i.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 5, + "sql_result_rows_count": 76, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the 10 articles most relevant to the topic of legislative processes as found on Wikipedia, and provide their titles, URLs, and associated image filenames and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Wikipedia article about legislative processes') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url, \n i.filename AS filename, \n i.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A historical photograph of a prominent political figure') AS ref_vec_0\n\nSELECT i.filename, h.heading_text, distance(i.description_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 8, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Unveil the tales told by three snapshots that echo the narrative of a historical giant in the realm of politics. What are their titles, and where do they reside in the archive?", + "external_knowledge": "Vector searches utilize models like \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" to understand and process both text and visual inputs. The MATCH operator conducts an approximate nearest neighbor search, which identifies items closely related to a specified concept or description. By specifying `k = 3`, the query narrows down the search to the top 3 most relevant images. These operations typically use Euclidean distance, meaning that similarity is inversely proportional to distance—the closer the vector representations in multi-dimensional space, the more similar they are considered. In this context, the vector search seeks images that convey or align with the idea of a \"historical photograph of a prominent political figure.\"", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A historical photograph of a prominent political figure') AS ref_vec_0\n\nSELECT i.filename, h.heading_text, distance(i.description_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A deep exploration of renewable energy sources and their impact on urban infrastructure') AS ref_vec_0,\n\nParagraphMatch AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE paragraph_index BETWEEN 1 AND 10\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, a.title, i.image_title\nFROM ParagraphMatch p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY p.distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Please find the three most relevant paragraphs about \"A deep exploration of renewable energy sources and their impact on urban infrastructure\", and provide their IDs, along with the titles of the articles and associated images. Ensure the paragraphs are from indices 1 to 10.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A deep exploration of renewable energy sources and their impact on urban infrastructure') AS ref_vec_0,\n\nParagraphMatch AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE paragraph_index BETWEEN 1 AND 10\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, a.title, i.image_title\nFROM ParagraphMatch p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY p.distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Significant historical events shaping our world') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%History%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM FilteredArticles fa\nJOIN Paragraphs p ON toString(fa.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Can you find a few paragraphs from articles on history that delve into major events that have shaped our world? Just send me their IDs.", + "external_knowledge": "In vector search operations, the \"MATCH\" operator performs an approximate nearest neighbor (ANN) search, which identifies items in the dataset that are closest in vector space to a specified query vector. This is often used to find semantically similar items. The parameter \"k = 5\" specifies that the top five matches should be returned. Euclidean distance is commonly used as the measure of similarity, meaning items with smaller distances are considered more similar to the query. The embedding model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is utilized to convert text into vectors, allowing the query to identify paragraphs most related to the concept of \"Significant historical events shaping our world.\"", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Significant historical events shaping our world') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%History%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM FilteredArticles fa\nJOIN Paragraphs p ON toString(fa.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Congress and legislative processes') AS ref_vec_0,\n\nArticleVectorSearch AS (\n SELECT \n article_id, \n title, \n distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ArticleVectorSearch.title AS title, \n Paragraphs.text AS text\nFROM ArticleVectorSearch\nJOIN Paragraphs ON toString(ArticleVectorSearch.article_id) = toString(Paragraphs.article_id)\nWHERE Paragraphs.paragraph_index = 0\nORDER BY ArticleVectorSearch.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 5 articles about the United States Congress and legislative processes, and provide their titles along with the text of the first paragraph. Make sure to order them starting with the most relevant ones!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Congress and legislative processes') AS ref_vec_0,\n\nArticleVectorSearch AS (\n SELECT \n article_id, \n title, \n distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ArticleVectorSearch.title AS title, \n Paragraphs.text AS text\nFROM ArticleVectorSearch\nJOIN Paragraphs ON toString(ArticleVectorSearch.article_id) = toString(Paragraphs.article_id)\nWHERE Paragraphs.paragraph_index = 0\nORDER BY ArticleVectorSearch.distance;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism that allows the President to appoint members of Congress to civil office without constitutional restrictions.') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few articles that discuss mechanisms like the Saxbe fix related to presidential appointments?", + "external_knowledge": "- **Vector Operations**: The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which identifies items that are similar to a given query based on vector embeddings.\n- **KNN Queries**: The parameter `k = 5` specifies that the search should return the top 5 most similar articles.\n- **Model and Embeddings**: The embedding model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' converts text into a vector format that captures semantic meaning, allowing for comparison based on content similarity.\n- **Domain Context**: The Saxbe fix is a legislative mechanism that addresses constitutional barriers regarding presidential appointments from Congress.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism that allows the President to appoint members of Congress to civil office without constitutional restrictions.') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of legislative procedures in government') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance \nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the top 5 articles related to the analysis of legislative procedures in government?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of legislative procedures in government') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance \nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy architecture feature') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Energy efficient pavilion view') AS ref_vec_1,\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelatedHeadings AS (\n SELECT h.heading_text\n FROM h_filtered AS h\n JOIN Image_Headings ih ON toString(h.heading_id) = toString(ih.heading_id)\n)\n\nSELECT i.description\nFROM i_filtered AS i;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "What are the descriptions of the top five images related to an energy-saving pavilion perspective?", + "external_knowledge": "- The `MATCH` operator is employed for approximate nearest neighbor (ANN) search, used here to find items that are semantically similar based on vector embeddings.\n- The `k=3` and `k=5` parameters specify that the query should return the top 3 headings and top 5 images that best match the given semantic descriptions, respectively.\n- The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is used, which is known for understanding the visual-textual relationship and is effective for tasks involving image and text embeddings.\n- In such vector searches, similarity is measured by the proximity of vector embeddings, typically using a Euclidean distance metric where a smaller distance indicates higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy architecture feature') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Energy efficient pavilion view') AS ref_vec_1,\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelatedHeadings AS (\n SELECT h.heading_text\n FROM h_filtered AS h\n JOIN Image_Headings ih ON toString(h.heading_id) = toString(ih.heading_id)\n)\n\nSELECT i.description\nFROM i_filtered AS i;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id,\n article_id,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT \n a.title AS title \nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance LIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Can you snag the article title that has a paragraph really close to the topic of \"Harper Lee’s To Kill a Mockingbird\"? I'm looking for the most spot-on match!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id,\n article_id,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT \n a.title AS title \nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in US constitutional law') AS ref_vec_0\n\nSELECT a.title, i.filename, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 35, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the top 5 articles related to \"The Saxbe fix and its implications in US constitutional law\" along with the filenames of their associated images?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in US constitutional law') AS ref_vec_0\n\nSELECT a.title, i.filename, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Congressional payment scheme') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelevantArticles AS (\n SELECT a.article_id, a.title\n FROM Articles a\n JOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\n),\n\nMatchingImages AS (\n SELECT i.article_id\n FROM i_filtered AS i\n)\n\nSELECT ra.title\nFROM RelevantArticles ra\nJOIN MatchingImages mi ON toString(ra.article_id) = toString(mi.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I want to find the titles of articles that include paragraphs highly related to the \"Congressional payment scheme\" and simultaneously contain images described as \"Portrait of Senator Edward Oliver Wolcott,\" selecting the top 5 matching articles based on both text and image descriptions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Congressional payment scheme') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelevantArticles AS (\n SELECT a.article_id, a.title\n FROM Articles a\n JOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\n),\n\nMatchingImages AS (\n SELECT i.article_id\n FROM i_filtered AS i\n)\n\nSELECT ra.title\nFROM RelevantArticles ra\nJOIN MatchingImages mi ON toString(ra.article_id) = toString(mi.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0,\n\nArticleCTE AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%solar energy%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN ArticleCTE a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the paragraph ID of the most relevant paragraph discussing \"Electricity generation from solar energy\" within articles whose titles include \"solar energy.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0,\n\nArticleCTE AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%solar energy%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN ArticleCTE a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism to avoid constitutional restrictions') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM SimilarArticles\nWHERE distance < 0.5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "What are some of the leading articles that might explore ways to bypass constitutional limitations?", + "external_knowledge": "In vector operations:\n- The `MATCH` operator is used to conduct an approximate nearest neighbor search, which is a method of finding the most similar items in terms of their vector representation.\n- The `k=5` parameter indicates the retrieval of the top 5 articles based on similarity from the embedding space defined by the vector model.\n- Similarity is assessed through Euclidean distance, where a smaller distance signifies higher similarity.\n- The phrase \"Mechanism to avoid constitutional restrictions\" serves as the conceptual target for the vector search, aiming to capture articles that are semantically aligned with this idea.\n- The threshold of `distance < 0.5` ensures that the articles are not only among the top five similar but also have a significant degree of relevance to this concept.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism to avoid constitutional restrictions') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM SimilarArticles\nWHERE distance < 0.5;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of the economic impact in the 21st century.') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed description of economic growth.') AS ref_vec_1,\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nImages_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, text, distance\n FROM Paragraphs_filtered AS Paragraphs BY distance\n),\n\nImageMatches AS (\n SELECT image_id, article_id, description, distance\n FROM Images_filtered AS Images BY distance\n)\n\nSELECT \n P.text AS paragraph_text,\n I.description AS image_description,\n A.title AS article_title\nFROM ParagraphMatches P\nJOIN ImageMatches I ON toString(P.article_id) = toString(I.article_id)\nJOIN Articles A ON toString(P.article_id) = toString(A.article_id)\nORDER BY P.distance, I.distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you find me the top 5 articles that feature a paragraph about the economic impact in the 21st century and an image describing economic growth? I'd love to see the titles, the paragraph texts, and image descriptions for those.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of the economic impact in the 21st century.') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed description of economic growth.') AS ref_vec_1,\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nImages_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, text, distance\n FROM Paragraphs_filtered AS Paragraphs BY distance\n),\n\nImageMatches AS (\n SELECT image_id, article_id, description, distance\n FROM Images_filtered AS Images BY distance\n)\n\nSELECT \n P.text AS paragraph_text,\n I.description AS image_description,\n A.title AS article_title\nFROM ParagraphMatches P\nJOIN ImageMatches I ON toString(P.article_id) = toString(I.article_id)\nJOIN Articles A ON toString(P.article_id) = toString(A.article_id)\nORDER BY P.distance, I.distance\nLIMIT 5;" + ], + "integration_level": 7, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 22307 ('BY') (line 27, col 46): BY distance\n),\n\nImageMatches AS (\n SELECT image_id, article_id, description, distance\n FROM Images_filtered AS Images BY distance\n)\n\nSELECT \n P.text AS. Expected one of: FINAL, SAMPLE, table, table function, subquery or list of joined tables, array join, LEFT ARRAY JOIN, INNER, ARRAY JOIN, GLOBAL, LOCAL, ANY, ALL, ASOF, SEMI, ANTI, ONLY, LEFT, RIGHT, FULL, CROSS, PASTE, JOIN, PREWHERE, WHERE, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Influential historical narrative') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, p.text, p.article_id\nFROM p_filtered AS p\nJOIN SimilarArticles sa ON toString(p.article_id) = toString(sa.article_id);", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "**\n\nCould you please find the top 5 articles that are related to historical events in the United States and then identify the paragraphs within those articles that align with an influential historical narrative? Make sure to include the paragraph IDs, the text of the paragraphs, and the article IDs for only those paragraphs that meet the specific condition of k being 3.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Influential historical narrative') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, p.text, p.article_id\nFROM p_filtered AS p\nJOIN SimilarArticles sa ON toString(p.article_id) = toString(sa.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative approaches in modern medicine') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A pioneering technique in healthcare') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarParagraphs AS (\n SELECT p.article_id, p.paragraph_id, p.text, p.distance\n FROM p_filtered AS p\n),\n\nSimilarImages AS (\n SELECT i.article_id, i.image_id, i.caption, i.distance\n FROM i_filtered AS i\n)\n\nSELECT a.title\nFROM Articles a\nWHERE a.article_id IN (\n SELECT sp.article_id\n FROM SimilarParagraphs sp\n UNION\n SELECT si.article_id\n FROM SimilarImages si\n)\nLIMIT 10;", + "sql_result_column_count": 1, + "sql_result_rows_count": 9, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Can you find some top articles that discuss innovative and pioneering breakthroughs in healthcare and modern medicine?", + "external_knowledge": "The query uses vector similarity search, which involves finding items that are most similar in context or meaning based on their transformed numeric representations (embeddings). In this context:\n\n- The `MATCH` operator is used to perform approximate nearest neighbor (ANN) searches for finding items that closely match a given concept or phrase.\n- The `k=5` parameter specifies that the query should return the top 5 paragraphs or captions that are closest to the specified phrases.\n- Vector models like 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' are employed to generate these embeddings, which capture semantic similarities.\n- Similarity is determined by the distance metric (usually Euclidean distance), where a smaller distance signifies a higher similarity.\n- Such vector searches are useful in retrieving data that isn't merely keyword-based but conceptually relevant.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative approaches in modern medicine') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A pioneering technique in healthcare') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarParagraphs AS (\n SELECT p.article_id, p.paragraph_id, p.text, p.distance\n FROM p_filtered AS p\n),\n\nSimilarImages AS (\n SELECT i.article_id, i.image_id, i.caption, i.distance\n FROM i_filtered AS i\n)\n\nSELECT a.title\nFROM Articles a\nWHERE a.article_id IN (\n SELECT sp.article_id\n FROM SimilarParagraphs sp\n UNION\n SELECT si.article_id\n FROM SimilarImages si\n)\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 558, server response: Code: 558. DB::Exception: Expected ALL or DISTINCT in SelectWithUnion query, because setting (union_default_mode) is empty: While processing SELECT sp.article_id FROM SimilarParagraphs AS sp UNION SELECT si.article_id FROM SimilarImages AS si. (EXPECTED_ALL_OR_DISTINCT) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An in-depth analysis of environmental impact and sustainable architecture.') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.raw_html LIKE '%Exelon%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you tell me the IDs of the top 5 paragraphs that discuss an in-depth analysis of environmental impact and sustainable architecture within articles mentioning \"Exelon\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An in-depth analysis of environmental impact and sustainable architecture.') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.raw_html LIKE '%Exelon%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of modern web development techniques') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Illustration of advanced programming concepts') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nTopArticles AS (\n SELECT article_id, title\n FROM Articles_filtered AS Articles\n)\n\nSELECT i.filename\nFROM i_filtered AS i\nJOIN TopArticles ta ON toString(i.article_id) = toString(ta.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the realm where bytes and pixels dance, can you unveil the files of those images that picture advanced programming concepts with a touch of magic, tied to the tales of modern web sorcery?", + "external_knowledge": "- The `MATCH` operator in the context of vector databases performs an approximate nearest neighbor (ANN) search, which efficiently finds entries that are semantically similar based on the provided embedding.\n- The term \"lembed\" refers to a function that uses a pre-trained language model to generate embeddings for text, capturing its semantic meaning.\n- The models, such as `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, are designed to map textual descriptions to a high-dimensional space where similar concepts are located closer together.\n- The clause `LIMIT 5` in the vector search indicates retrieving the top 5 most relevant articles, emphasizing a selection of articles that best fit the specified theme.\n- L2 norm (Euclidean distance) is generally used to compute the closeness between vectors, meaning that shorter distances imply a higher degree of similarity.\n- The condition `i.k = 3` implies a specific constraint or attribute of the images which might be domain-specific (e.g., an identifier or a category).", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of modern web development techniques') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Illustration of advanced programming concepts') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nTopArticles AS (\n SELECT article_id, title\n FROM Articles_filtered AS Articles\n)\n\nSELECT i.filename\nFROM i_filtered AS i\nJOIN TopArticles ta ON toString(i.article_id) = toString(ta.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms of congressional appointments and constitutional restrictions') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison proposed language at the Constitutional Convention to prevent ethical conflicts.') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n WHERE raw_wikitext_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms of congressional appointments AND constitutional restrictions')\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarArticles AS (\n SELECT \n a.article_id AS article_id,\n a.title AS title,\n a.url AS url,\n distance\n FROM a_filtered AS a\n ORDER BY \n distance\n)\n\nSELECT \n sa.article_id AS article_id \nFROM \n SimilarArticles sa\nJOIN p_filtered AS p ON toString(sa.article_id) = toString(p.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Can you help me identify a handful of articles related to congressional appointment mechanisms and ethical guidelines, particularly focusing on ideas proposed by James Madison?", + "external_knowledge": "The `MATCH` operator used in the SQL query performs an approximate nearest neighbor (ANN) search to find items similar to a given vector representation. The `k=5` clause specifies that the search will return the top 5 most similar items. The similarity is determined using the Euclidean distance metric, where a smaller distance indicates higher similarity.\n\nIn this context, the vector representations are generated using the embedding model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, which is designed to capture semantic meanings in texts and images. This model is particularly useful for identifying thematic similarities between text passages despite variations in wording.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms of congressional appointments and constitutional restrictions') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison proposed language at the Constitutional Convention to prevent ethical conflicts.') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n WHERE raw_wikitext_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms of congressional appointments AND constitutional restrictions')\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarArticles AS (\n SELECT \n a.article_id AS article_id,\n a.title AS title,\n a.url AS url,\n distance\n FROM a_filtered AS a\n ORDER BY \n distance\n)\n\nSELECT \n sa.article_id AS article_id \nFROM \n SimilarArticles sa\nJOIN p_filtered AS p ON toString(sa.article_id) = toString(p.article_id);" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 21942 ('MATCH') (line 10, col 34): MATCH [-0.4506005048751831, 0.3593186140060425, 0.18939214944839478, 0.27233511209487915, -0.1898040920495987, -0.0290442556142807, 0.32632091641426086, 0.09055. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Without regard to the constitutional issue') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 1\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT i.image_title\nFROM i_filtered AS i\nJOIN p_filtered AS p ON toString(i.article_id) = toString(p.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you help me track down the image titles for the best match images that are described as \"Portrait of Senator Edward Oliver Wolcott\" and belong to articles containing paragraphs related to \"Without regard to the constitutional issue\"? Thanks a bunch!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Without regard to the constitutional issue') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 1\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT i.image_title\nFROM i_filtered AS i\nJOIN p_filtered AS p ON toString(i.article_id) = toString(p.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "SELECT article_id\nFROM Articles\nWHERE raw_html_embedding MATCH \n (SELECT raw_html_embedding \n FROM Articles \n WHERE title = 'Saxbe fix')\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the article whose content is most closely related to the article titled 'Saxbe fix'? I need you to return the article's ID, and make sure to limit the result to the closest match!", + "external_knowledge": "", + "sql_candidate": [ + "SELECT article_id\nFROM Articles\nWHERE raw_html_embedding MATCH \n (SELECT raw_html_embedding \n FROM Articles \n WHERE title = 'Saxbe fix')\nLIMIT 1;" + ], + "integration_level": 0, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 58 ('MATCH') (line 3, col 26): MATCH \n (SELECT raw_html_embedding \n FROM Articles \n WHERE title = 'Saxbe fix')\nLIMIT 1\n FORMAT Native. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT, INTO OUTFILE, FORMAT, end of query. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical overview of U.S. constitutional law') AS ref_vec_0\n\nSELECT a.article_id, a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Senate%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Please find the top 5 articles that provide a historical overview of U.S. constitutional law and include images with descriptions mentioning the Senate. Return their IDs, titles, and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical overview of U.S. constitutional law') AS ref_vec_0\n\nSELECT a.article_id, a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Senate%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id\n FROM Articles\n WHERE title LIKE '%Exelon%'\n)\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nWHERE article_id IN (SELECT article_id FROM FilteredArticles)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Could you please find the three most relevant paragraphs related to \"Four buildings that generate electricity from solar energy\" from articles that mention \"Exelon\"? I need to know their paragraph IDs!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id\n FROM Articles\n WHERE title LIKE '%Exelon%'\n)\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nWHERE article_id IN (SELECT article_id FROM FilteredArticles)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 6, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 49, server response: Code: 49. DB::Exception: Not-ready Set is passed as the second argument for function 'in': while executing 'FUNCTION in(article_id : 0, _subquery30 :: 1) -> in(article_id, _subquery30) Nullable(UInt8) : 2': While executing MergeTreeSelect(pool: ReadPoolInOrder, algorithm: InOrder). (LOGICAL_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park solar energy structures') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy generation in Chicago') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance \n FROM Articles_filtered AS Articles\n)\n\nSELECT sa.article_id, p.paragraph_id\nFROM SimilarArticles sa\nJOIN p_filtered AS p ON toString(sa.article_id) = toString(p.article_id);", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Amidst the architectural marvels of Millennium Park, what are the top 5 articles that weave tales of its solar energy structures? And within these narratives, can you uncover the top 3 passages that echo the harmonious symphony of solar energy generation in the heart of Chicago?", + "external_knowledge": "In vector-based searches like those used in this query, the `MATCH` operator performs an approximate nearest neighbor search, tapping into the power of vector embeddings to find closely related items based on their semantic meanings. Here, the `lembed` function utilizes the \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" model to transform text inputs into embeddings that capture their semantic essence. The parameter `k` specifies the number of top similar items to return, with `k = 5` for articles and `k = 3` for paragraphs in this query. The similarity between vectors is typically measured using Euclidean distance, where a smaller distance indicates a stronger similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park solar energy structures') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy generation in Chicago') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance \n FROM Articles_filtered AS Articles\n)\n\nSELECT sa.article_id, p.paragraph_id\nFROM SimilarArticles sa\nJOIN p_filtered AS p ON toString(sa.article_id) = toString(p.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, a.title, a.url, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 17818377\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 6, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Please provide the paragraph ID, article ID, text, title, URL, and similarity distance for the top 5 paragraphs related to \"Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch,\" specifically from articles that are associated with the Wikipedia ID 17818377.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, a.title, a.url, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 17818377\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful paragraph about constitutional laws and their implications on public office appointments') AS ref_vec_0\n\nSELECT a.title, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the titles and paragraph indices of the top 10 paragraphs related to constitutional laws and their implications on public office appointments, as found in the articles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful paragraph about constitutional laws and their implications on public office appointments') AS ref_vec_0\n\nSELECT a.title, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on ethical conflicts in the government') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Ineligibility Clause%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Please identify the top 5 paragraphs that discuss ethical conflicts in the government, ensuring they are from articles with the title \"Ineligibility Clause\". I need their paragraph IDs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on ethical conflicts in the government') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Ineligibility Clause%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative architecture and renewable energy technologies') AS ref_vec_0,\n\nArticleMeta AS (\n SELECT key, value\n FROM Articles_info\n WHERE key IN ('author', 'publication_date')\n)\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN ArticleMeta am ON toString(a.article_id) = toString(am.value)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 5 articles related to innovative architecture and renewable energy technologies?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative architecture and renewable energy technologies') AS ref_vec_0,\n\nArticleMeta AS (\n SELECT key, value\n FROM Articles_info\n WHERE key IN ('author', 'publication_date')\n)\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN ArticleMeta am ON toString(a.article_id) = toString(am.value)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table wikipedia_multimodal.Articles_info does not exist. Maybe you meant ai_and_technology_news_aggregation_and_analysis.ARTICLE_TAGS?. (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison and ethical conflicts in political appointments') AS ref_vec_0,\n\nRelatedParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelatedParagraphs;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to find the top 5 paragraphs that discuss James Madison and ethical conflicts in political appointments. Please provide their textual content, sorted by relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison and ethical conflicts in political appointments') AS ref_vec_0,\n\nRelatedParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelatedParagraphs;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy projects in urban areas') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nWHERE h.heading_text = 'Background'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the titles of the top 5 articles discussing the background on renewable energy projects in urban areas?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy projects in urban areas') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nWHERE h.heading_text = 'Background'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events') AS ref_vec_0,\n\nRelevantHeadings AS (\n SELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.description\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN RelevantHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY rh.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "What is the description of the image that paints the clearest picture of historical events?", + "external_knowledge": "In the realm of vector operations, the `MATCH` operator is used for performing approximate nearest neighbor (ANN) searches, which are efficient for finding items similar to a given concept. The `lembed` function generates a vector representation of the text \"Historical events,\" which allows the system to capture semantic meanings. The `k = 5` clause indicates the query is interested in the top 5 closest matches based on Euclidean distance, with smaller distances indicating higher similarity. The approach leverages embeddings from the model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', which is fine-tuned for understanding visual and textual content.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events') AS ref_vec_0,\n\nRelevantHeadings AS (\n SELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.description\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN RelevantHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY rh.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of a historical figure in the United States Senate') AS ref_vec_0\n\nSELECT i.image_id, distance(i.description_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you find me the image of a historical figure who's been in the United States Senate? I need the top match for this!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of a historical figure in the United States Senate') AS ref_vec_0\n\nSELECT i.image_id, distance(i.description_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'description_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and Millennium Park in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and Millennium Park in Chicago') AS ref_vec_1,\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy AND Millennium Park in Chicago')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n WHERE raw_wikitext_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy AND Millennium Park in Chicago')\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredParagraphs AS (\n SELECT paragraph_id, article_id\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.title, a.url\nFROM a_filtered AS a\nJOIN FilteredParagraphs fp ON toString(a.article_id) = toString(fp.article_id);", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Top 5 articles related to \"solar energy and Millennium Park in Chicago\", return their titles and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and Millennium Park in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and Millennium Park in Chicago') AS ref_vec_1,\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy AND Millennium Park in Chicago')\n ORDER BY distance\n LIMIT 5\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n WHERE raw_wikitext_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy AND Millennium Park in Chicago')\n ORDER BY distance\n LIMIT 5\n),\n\nFilteredParagraphs AS (\n SELECT paragraph_id, article_id\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.title, a.url\nFROM a_filtered AS a\nJOIN FilteredParagraphs fp ON toString(a.article_id) = toString(fp.article_id);" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 21961 ('MATCH') (line 10, col 26): MATCH [0.06277919560670853, 0.10719381272792816, 0.5015848875045776, -0.39275091886520386, -0.12658840417861938, 0.09521721303462982, 0.1399870216846466, -0.203. Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "SELECT article_id, title FROM Articles;", + "sql_result_column_count": 2, + "sql_result_rows_count": 100, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Could you pull up all the articles for me? I'm curious to see their IDs and titles. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "SELECT article_id, title FROM Articles;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter appoints Edmund Muskie as Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you show me the IDs of the top 3 images that have captions related to President Carter appointing Edmund Muskie as Secretary of State?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter appoints Edmund Muskie as Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Photograph of a historical figure from the early 20th century') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you provide the IDs and similarity scores for the 10 images that best match the description \"Photograph of a historical figure from the early 20th century\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Photograph of a historical figure from the early 20th century') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and environmental impact') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE wiki_id = 1\n)\n\nSELECT p.paragraph_id, f.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN FilteredArticles f ON toString(p.article_id) = toString(f.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the titles and paragraph IDs of the top three paragraphs related to solar energy and environmental impact, specifically from articles belonging to wiki ID 1.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and environmental impact') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE wiki_id = 1\n)\n\nSELECT p.paragraph_id, f.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN FilteredArticles f ON toString(p.article_id) = toString(f.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth in literature') AS ref_vec_0,\n\nMatchedParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.url\nFROM Articles a\nJOIN MatchedParagraphs mp ON toString(a.article_id) = toString(mp.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Can you uncover the web addresses of the top three articles that delve into the journey of understanding racial injustice and the path of moral evolution through the lens of literature?", + "external_knowledge": "The `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search, which is a common technique for finding data points that are most similar to a given vector representation. The `k=3` specifies that the query should return the top 3 most similar paragraphs to the given concept. The similarity is determined based on the Euclidean distance (L2 norm), where smaller distances indicate higher similarity. The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is used to generate the embeddings, which are then used to encapsulate the concept of racial injustice and moral growth in literature.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth in literature') AS ref_vec_0,\n\nMatchedParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.url\nFROM Articles a\nJOIN MatchedParagraphs mp ON toString(a.article_id) = toString(mp.article_id);" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The mechanism of Saxbe fix in the United States Constitution') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please identify the top 5 paragraphs that closely relate to the mechanism of Saxbe fix in the United States Constitution? I need their paragraph IDs, the article IDs they belong to, and their positions within those articles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The mechanism of Saxbe fix in the United States Constitution') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the United States Congress') AS ref_vec_0\n\nSELECT \n p.paragraph_id AS paragraph_id, \n a.title AS title, \n a.url AS url, \n distance(p.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs p\nJOIN \n Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please identify the top 5 paragraphs that are highly related to the concept of \"The Saxbe fix\" and its impact on the United States Congress? Also, get me the article titles and URLs for these paragraphs, and let me know their similarity distances!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the United States Congress') AS ref_vec_0\n\nSELECT \n p.paragraph_id AS paragraph_id, \n a.title AS title, \n a.url AS url, \n distance(p.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs p\nJOIN \n Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has become a relevant solution for appointments to the United States Cabinet.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 3 paragraphs that talk about how the Saxbe fix is a relevant solution for Cabinet appointments in the US? I'd love to know their IDs and the articles they're from!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has become a relevant solution for appointments to the United States Cabinet.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "What can you tell me about the three paragraphs that are closely related to the themes of Harper Lee's \"To Kill a Mockingbird,\" especially focusing on racial injustice and growth through Scout Finch's perspective?", + "external_knowledge": "Vector operations in this context involve using embeddings to conduct a nearest neighbor search. The \"MATCH\" operator is utilized to perform an approximate nearest neighbor (ANN) search based on the description's embedding vector. The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` generates a vector representation of the specified text, which is then compared to the vectors in the `text_embedding` column. The `k = 3` parameter ensures that only the top three paragraphs with the smallest Euclidean distances are returned, highlighting those most similar in themes of racial injustice and moral growth as depicted through the eyes of Scout Finch.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms for adjusting constitutional appointments, focusing on historical processes') AS ref_vec_0,\n\nArticleSelection AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT article_id\nFROM ArticleSelection\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you provide the IDs of the top 5 articles that focus on historical processes for adjusting constitutional appointments?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms for adjusting constitutional appointments, focusing on historical processes') AS ref_vec_0,\n\nArticleSelection AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT article_id\nFROM ArticleSelection\nORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional debates and historical legislative procedures') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, p.text\nFROM RelevantParagraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Constitution%'\nORDER BY p.distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you give me the titles and URLs of articles related to \"Constitution\" and provide the top 5 paragraphs that discuss constitutional debates and historical legislative procedures? These paragraphs should be ranked by their relevance to the topic.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional debates and historical legislative procedures') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, p.text\nFROM RelevantParagraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Constitution%'\nORDER BY p.distance\nLIMIT 5;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional mechanism to appoint current or former Congress members') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title = 'Saxbe fix'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Seek the quintessence of thought: What are the five most insightful paragraphs discussing the constitutional dance of appointing current or former Congress members, drawn from the article known as \"Saxbe fix\"?", + "external_knowledge": "In this query, the `MATCH` operator is used to perform an approximate nearest neighbor search to find paragraphs whose text embeddings are similar to the given concept. The `k=5` specifies that the top 5 closest matches are returned. The model \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" is used to generate embeddings, allowing textual data to be compared in a high-dimensional vector space. The closer the paragraphs' embeddings are to the specified concept, the higher they rank in similarity, which is determined using Euclidean distance (L2 norm).", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional mechanism to appoint current or former Congress members') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title = 'Saxbe fix'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of a historical figure') AS ref_vec_0\n\nSELECT i.image_title, h.heading_text, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the titles and headings of the 5 images most closely related to \"Portrait of a historical figure\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of a historical figure') AS ref_vec_0\n\nSELECT i.image_title, h.heading_text, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'caption_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on legal precedents in the United States') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title, p.text\nFROM FilteredArticles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index = 0\nORDER BY a.distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Top 3 articles discussing legal precedents in the United States, return their titles and first paragraphs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on legal precedents in the United States') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title, p.text\nFROM FilteredArticles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index = 0\nORDER BY a.distance\nLIMIT 3;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative use of renewable energy and green design') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title, \n a.url AS url, \n sp.text AS text\nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance AS distance \nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 3 articles that have paragraphs discussing innovative renewable energy and green design? I need to know the articles' titles, URLs, and those paragraph snippets. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative use of renewable energy and green design') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title, \n a.url AS url, \n sp.text AS text\nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance AS distance \nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', '\\n \\n History of Saxbe fix\\n \\n

Saxbe fix article

\\n

This article provides details about the Saxbe fix, a mechanism used by presidents

\\n \\n ') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Please provide the ID and title of the article that best matches a description of the Saxbe fix, as described in the provided HTML content.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', '\\n \\n History of Saxbe fix\\n \\n

Saxbe fix article

\\n

This article provides details about the Saxbe fix, a mechanism used by presidents

\\n \\n ') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a constitutional mechanism dealing with emoluments.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT a.title\nFROM RelevantParagraphs rp\nJOIN Articles a ON toString(rp.article_id) = toString(a.article_id)\nORDER BY rp.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the realm of constitutional mechanisms, find the title of the article that best embodies the concept of the Saxbe fix, a solution regarding emoluments.", + "external_knowledge": "Vector operations using the `MATCH` operator perform an approximate nearest neighbor (ANN) search to find items that are most similar to a particular vector representation. The `lembed()` function is used to derive embeddings from text based on specific pre-trained models like 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'. The search process ranks items by their Euclidean distance from the target embedding, with smaller distances indicating higher similarity. The Saxbe fix relates to the legal strategy used to circumvent emoluments clauses within the U.S. Constitution.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a constitutional mechanism dealing with emoluments.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT a.title\nFROM RelevantParagraphs rp\nJOIN Articles a ON toString(rp.article_id) = toString(a.article_id)\nORDER BY rp.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Ineligibility Clause prevents members of Congress from taking civil office positions created or whose emoluments are increased during their term.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelevantParagraphs\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the texts of the 5 paragraphs most relevant to the concept of the Ineligibility Clause that prevents members of Congress from taking certain civil office positions, ordered by their relevance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Ineligibility Clause prevents members of Congress from taking civil office positions created or whose emoluments are increased during their term.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelevantParagraphs\nORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy structures in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy and architectural design') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy AND architectural design')\n ORDER BY distance\n LIMIT 3\n),\n\nArticleCTE AS (\n SELECT article_id\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.text\nFROM p_filtered AS p\nJOIN ArticleCTE a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the paragraph from articles related to solar energy structures in Chicago that best discusses renewable energy and architectural design, specifying a particular focus or level, and present the most relevant one.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy structures in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy and architectural design') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n WHERE text_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy AND architectural design')\n ORDER BY distance\n LIMIT 3\n),\n\nArticleCTE AS (\n SELECT article_id\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.text\nFROM p_filtered AS p\nJOIN ArticleCTE a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 22128 ('MATCH') (line 20, col 26): MATCH [0.02829970419406891, 0.11518597602844238, -0.08965106308460236, -0.21492013335227966, -0.046713218092918396, -0.05969109386205673, -0.11051450669765472, . Expected one of: token sequence, Dot, token, OR, AND, IS NOT DISTINCT FROM, IS NULL, IS NOT NULL, BETWEEN, NOT BETWEEN, LIKE, ILIKE, NOT LIKE, NOT ILIKE, REGEXP, IN, NOT IN, GLOBAL IN, GLOBAL NOT IN, MOD, DIV, alias, AS, GROUP BY, WITH, HAVING, WINDOW, QUALIFY, ORDER BY, LIMIT, OFFSET, FETCH, SETTINGS, UNION, EXCEPT, INTERSECT. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause in the Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix salary rollback mechanism') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nRelevantArticles AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n),\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance AS paragraph_distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id\nFROM RelevantArticles a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.article_distance + p.paragraph_distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles that talk about the \"Ineligibility Clause\" and have some stuff on the \"Saxbe fix salary rollback mechanism\"? I need them ordered by how well they fit both topics!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause in the Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix salary rollback mechanism') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nRelevantArticles AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n),\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance AS paragraph_distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id\nFROM RelevantArticles a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.article_distance + p.paragraph_distance\nLIMIT 5;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy projects in urban areas') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Images showcasing solar panels and wind turbines') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n WHERE description_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Images showcasing solar panels\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, i.image_title\nFROM a_filtered AS a\nJOIN i_filtered AS i ON toString(a.article_id) = toString(i.article_id)\n WHERE wind turbines');", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Please provide the titles of articles and their associated image titles for the top 5 articles focused on renewable energy projects in urban areas, along with the top 5 images showcasing solar panels and wind turbines.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy projects in urban areas') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Images showcasing solar panels and wind turbines') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n WHERE description_embedding MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Images showcasing solar panels\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, i.image_title\nFROM a_filtered AS a\nJOIN i_filtered AS i ON toString(a.article_id) = toString(i.article_id)\n WHERE wind turbines');" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 22008 ('(') (line 15, col 15): (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n WHERE description_embedding MATCH lembed('laion/CLIP-. Unmatched parentheses: (. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', '\\nMillennium Park') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy, located in Millennium Park.') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of a senator known for environmental initiatives.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM ArticleMatches\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM ParagraphMatches\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_2) AS distance\n FROM ImageMatches\n\n ORDER BY distance\n LIMIT 5\n),\n\nArticleMatches AS (\n SELECT a.article_id, a.title, a.url, a.raw_html, a.raw_wikitext, a.raw_html_embedding, a.raw_wikitext_embedding, distance AS html_distance\n FROM a_filtered AS a\n),\n\nParagraphMatches AS (\n SELECT p.paragraph_id, p.article_id, p.text, p.text_embedding, distance AS paragraph_distance\n FROM p_filtered AS p\n),\n\nImageMatches AS (\n SELECT i.image_id, i.article_id, i.description, i.description_embedding, distance AS image_distance\n FROM i_filtered AS i\n)\n\nSELECT a.title\nFROM ArticleMatches a\nJOIN ParagraphMatches p ON toString(a.article_id) = toString(p.article_id)\nJOIN ImageMatches i ON toString(a.article_id) = toString(i.article_id)\nORDER BY a.html_distance + p.paragraph_distance + i.image_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the article title from the database that is most representative based on three criteria: having HTML content related to Millennium Park, containing paragraphs about solar energy buildings in Millennium Park, and including images described as portraits of a senator known for environmental initiatives. Only one article with the closest match across all criteria should be returned.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', '\\nMillennium Park') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Four buildings that generate electricity from solar energy, located in Millennium Park.') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of a senator known for environmental initiatives.') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM ArticleMatches\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM ParagraphMatches\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_2) AS distance\n FROM ImageMatches\n\n ORDER BY distance\n LIMIT 5\n),\n\nArticleMatches AS (\n SELECT a.article_id, a.title, a.url, a.raw_html, a.raw_wikitext, a.raw_html_embedding, a.raw_wikitext_embedding, distance AS html_distance\n FROM a_filtered AS a\n),\n\nParagraphMatches AS (\n SELECT p.paragraph_id, p.article_id, p.text, p.text_embedding, distance AS paragraph_distance\n FROM p_filtered AS p\n),\n\nImageMatches AS (\n SELECT i.image_id, i.article_id, i.description, i.description_embedding, distance AS image_distance\n FROM i_filtered AS i\n)\n\nSELECT a.title\nFROM ArticleMatches a\nJOIN ParagraphMatches p ON toString(a.article_id) = toString(p.article_id)\nJOIN ImageMatches i ON toString(a.article_id) = toString(i.article_id)\nORDER BY a.html_distance + p.paragraph_distance + i.image_distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 60, server response: Code: 60. DB::Exception: Table wikipedia_multimodal.ArticleMatches does not exist. Maybe you meant ai_and_technology_news_aggregation_and_analysis.ARTICLE_TAGS?. (UNKNOWN_TABLE) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in the United States constitutional law') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 127, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a handful of sections discussing the Saxbe fix's impact on U.S. constitutional law?", + "external_knowledge": "The vector search performed by the `MATCH` operator involves comparing the vector representation of article content against a query vector generated from the text \"The Saxbe fix and its implications in the United States constitutional law\". The `lembed` function utilizes a specific model ('laion/CLIP-ViT-B-32-laion2B-s34B-b79K') to transform text into vectors, allowing for semantic similarity comparison. The parameter `k=5` indicates that the search should return the top 5 items with the highest similarity. Typically, in vector searches, lower Euclidean distances between vectors represent higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in the United States constitutional law') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed architectural overview with environmental design elements in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into the sustainable practices used in modern architecture') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.article_id, p.paragraph_index\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.distance, p.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Find the IDs and paragraph indices for the top 3 articles about architectural design in Chicago and the top 5 paragraphs on sustainable practices in modern architecture.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed architectural overview with environmental design elements in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into the sustainable practices used in modern architecture') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.article_id, p.paragraph_index\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.distance, p.distance\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix was named after Senator William Saxbe') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism reducing emoluments for cabinet appointments') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT article_id, title, url, raw_html_embedding, distance\n FROM Articles_filtered AS Articles\n),\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, paragraph_index, text_embedding, distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id, a.title, pm.paragraph_index, pm.distance AS paragraph_distance\nFROM ArticleMatches a\nJOIN ParagraphMatches pm ON toString(a.article_id) = toString(pm.article_id)\nORDER BY pm.distance\nLIMIT 10;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 10 articles and their titles, along with the corresponding paragraph index and similarity distance, where the articles are related to Senator William Saxbe and the paragraphs discuss reducing emoluments for cabinet appointments. Make sure to order them by paragraph similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix was named after Senator William Saxbe') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism reducing emoluments for cabinet appointments') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT article_id, title, url, raw_html_embedding, distance\n FROM Articles_filtered AS Articles\n),\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, paragraph_index, text_embedding, distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id, a.title, pm.paragraph_index, pm.distance AS paragraph_distance\nFROM ArticleMatches a\nJOIN ParagraphMatches pm ON toString(a.article_id) = toString(pm.article_id)\nORDER BY pm.distance\nLIMIT 10;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice in American literature') AS ref_vec_0\n\nSELECT a.title, a.url, i.url AS image_url, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 10, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the great library of knowledge, uncover the top 10 chronicles that delve into the depths of racial injustice in American literature, bringing with them tales of titles, pathways to their wisdom, and the distance they traversed to meet this quest.", + "external_knowledge": "- The `MATCH` operator is utilized to perform an approximate nearest neighbor (ANN) search, which ranks documents based on their semantic similarity to a given concept.\n- The `k = 5` parameter specifies that the top 5 most similar paragraphs are selected for each article, based on the embedding.\n- The Euclidean distance (L2 norm) is employed as a measure of similarity, where smaller distances indicate a closer semantic match.\n- The use of 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' leverages a powerful model adept at understanding textual and visual contexts, especially valuable in thematic exploration such as racial injustice in literature.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice in American literature') AS ref_vec_0\n\nSELECT a.title, a.url, i.url AS image_url, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'text_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Architectural building with modern design and solar energy features') AS ref_vec_0\n\nSELECT a.title, a.url, COUNT(i.image_id) AS image_count, av.distance, distance((.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nJOIN (\n SELECT article_id, distance \n FROM Articles \n \n \n) AS av ON toString(a.article_id) = toString(av.article_id)\nGROUP BY a.article_id, av.distance\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles, URLs, and image counts of the top 10 articles related to architectural buildings with a modern design and solar energy features, along with their relevance distances?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Architectural building with modern design and solar energy features') AS ref_vec_0\n\nSELECT a.title, a.url, COUNT(i.image_id) AS image_count, av.distance, distance((.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nJOIN (\n SELECT article_id, distance \n FROM Articles \n \n \n) AS av ON toString(a.article_id) = toString(av.article_id)\nGROUP BY a.article_id, av.distance\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 9, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 62, server response: Code: 62. DB::Exception: Syntax error: failed at position 10993 ('(') (line 4, col 79): ((.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nJOIN (\n SELECT article_id, d. Unmatched parentheses: (. (SYNTAX_ERROR) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed discussion on United States constitutional law focusing on legislative processes.') AS ref_vec_0,\n\nEmbeddingSearch AS (\n SELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT article_id\nFROM EmbeddingSearch;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you fetch me the article ID for the top piece that dives deep into U.S. constitutional law and legislative processes? Just need the one that's the best fit!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed discussion on United States constitutional law focusing on legislative processes.') AS ref_vec_0,\n\nEmbeddingSearch AS (\n SELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT article_id\nFROM EmbeddingSearch;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President appoints ambassador during constitutional debate') AS ref_vec_0\n\nSELECT \n i.image_id AS image_id, \n i.filename AS filename, \n h.heading_text, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM \n Images i\nJOIN \n Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN \n Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 6, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you provide me with the filenames and heading texts of the top 3 images related to the topic \"President appoints ambassador during constitutional debate\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President appoints ambassador during constitutional debate') AS ref_vec_0\n\nSELECT \n i.image_id AS image_id, \n i.filename AS filename, \n h.heading_text, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM \n Images i\nJOIN \n Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN \n Headings h ON toString(ih.heading_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'caption_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Understanding the complexities of quantum mechanics and its implications') AS ref_vec_0,\n\nParagraphSimilarities AS (\n SELECT p.paragraph_id, p.article_id, distance(p.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN ParagraphSimilarities ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the article title that is most related to the understanding of the complexities of quantum mechanics and its implications?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Understanding the complexities of quantum mechanics and its implications') AS ref_vec_0,\n\nParagraphSimilarities AS (\n SELECT p.paragraph_id, p.article_id, distance(p.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN ParagraphSimilarities ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technological advancements in AI') AS ref_vec_0\n\nSELECT a.title, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the titles and texts of the top 5 paragraphs related to technological advancements in AI?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technological advancements in AI') AS ref_vec_0\n\nSELECT a.title, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of renewable energy sources and their impact') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the top 5 paragraphs that discuss the exploration of renewable energy sources and their impact, including their IDs, article IDs, and positions within the articles?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of renewable energy sources and their impact') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the paragraph ID and text of the paragraph that best represents \"Harper Lee's To Kill a Mockingbird\" from the Paragraphs table.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause') AS ref_vec_0,\n\nArticleMatches AS (\n SELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT p.text\nFROM Paragraphs p\nJOIN ArticleMatches am ON toString(p.article_id) = toString(am.article_id)\nORDER BY am.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you grab me the paragraph from the article that's closest to the topic \"Ineligibility Clause\"? I'm looking for the top matching article's text!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause') AS ref_vec_0,\n\nArticleMatches AS (\n SELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT p.text\nFROM Paragraphs p\nJOIN ArticleMatches am ON toString(p.article_id) = toString(am.article_id)\nORDER BY am.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy and its impact') AS ref_vec_0\n\nSELECT \n a.title AS article_title,\n p.text AS paragraph_text,\n i.caption AS image_caption,\n distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 10, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Please identify the top 5 paragraphs related to the topic of renewable energy and its impact. For each, provide their associated article title, paragraph text, image caption, and similarity distance, sorted by proximity. Limit the results to the 10 most relevant findings.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy and its impact') AS ref_vec_0\n\nSELECT \n a.title AS article_title,\n p.text AS paragraph_text,\n i.caption AS image_caption,\n distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "failed", + "error_message": "Received ClickHouse exception, code: 47, server response: Code: 47. DB::Exception: There is no column 'text_embedding' in table '--.t'. (UNKNOWN_IDENTIFIER) (version 24.8.8.1) (for url http://112.126.57.89:8123)", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);" + } +] \ No newline at end of file diff --git a/benchmark/data/results/wikipedia_multimodal/input_llm.json b/benchmark/data/results/wikipedia_multimodal/input_llm.json new file mode 100644 index 0000000..9022896 --- /dev/null +++ b/benchmark/data/results/wikipedia_multimodal/input_llm.json @@ -0,0 +1,2422 @@ +[ + { + "db_id": "wikipedia_multimodal", + "sql": "SELECT title FROM Articles;", + "sql_result_column_count": 1, + "sql_result_rows_count": 100, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you give me the list of all the article titles you've got in the database?", + "external_knowledge": "", + "sql_candidate": [ + "SELECT title FROM Articles;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you give me the list of all the article titles you've got in the database?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Featured article about energy-efficient buildings in Chicago') AS ref_vec_0\n\nSELECT title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you find the top article that discusses energy-efficient buildings in Chicago and give me its title?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Featured article about energy-efficient buildings in Chicago') AS ref_vec_0\n\nSELECT title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you find the top article that discusses energy-efficient buildings in Chicago and give me its title?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe amendment controversy') AS ref_vec_0\n\nSELECT heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey, can you find me the most relevant heading related to the Saxbe amendment controversy? Just need the text of the best one!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe amendment controversy') AS ref_vec_0\n\nSELECT heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey, can you find me the most relevant heading related to the Saxbe amendment controversy? Just need the text of the best one!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'architecture and sustainable design in urban spaces') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you fetch me the article that's all about architecture and sustainable design in urban spaces? I only need the top one, okay?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'architecture and sustainable design in urban spaces') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you fetch me the article that's all about architecture and sustainable design in urban spaces? I only need the top one, okay?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced architectural design in modern municipal buildings in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Chicago%'\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Find the title of the article that is the best match for advanced architectural design in modern municipal buildings in Chicago, and ensure the article is associated with an image description containing the keyword \"Chicago\".", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced architectural design in modern municipal buildings in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Chicago%'\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nFind the title of the article that is the best match for advanced architectural design in modern municipal buildings in Chicago, and ensure the article is associated with an image description containing the keyword \"Chicago\".\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Can you show me the article ID and title of the article that is most relevant to electricity generation from solar energy?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you show me the article ID and title of the article that is most relevant to electricity generation from solar energy?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of Chicago''''s architectural significance in modern history.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you find the paragraph ID that best describes the exploration of Chicago's architectural significance in modern history?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of Chicago''''s architectural significance in modern history.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you find the paragraph ID that best describes the exploration of Chicago's architectural significance in modern history?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'ethical conflicts in governance') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Could you tell me the IDs of the top 5 paragraphs that most closely align with the topic of ethical conflicts in governance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'ethical conflicts in governance') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the IDs of the top 5 paragraphs that most closely align with the topic of ethical conflicts in governance?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'sustainable architecture and green building practices') AS ref_vec_0\n\nSELECT p.article_id, p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the article and paragraph IDs for the 5 paragraphs most related to sustainable architecture and green building practices?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'sustainable architecture and green building practices') AS ref_vec_0\n\nSELECT p.article_id, p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat are the article and paragraph IDs for the 5 paragraphs most related to sustainable architecture and green building practices?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into solar energy and architecture in Chicago') AS ref_vec_0,\n\nFilteredParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM FilteredParagraphs fp\nJOIN Articles a ON toString(fp.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 123;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! I'm looking for the top 5 paragraphs from articles on Wikipedia about solar energy and architecture in Chicago. Can you find those for me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into solar energy and architecture in Chicago') AS ref_vec_0,\n\nFilteredParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM FilteredParagraphs fp\nJOIN Articles a ON toString(fp.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 123;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! I'm looking for the top 5 paragraphs from articles on Wikipedia about solar energy and architecture in Chicago. Can you find those for me?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter and Edmund Muskie') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Please identify the image associated with the top caption that most closely represents \"President Carter and Edmund Muskie\" from the Images table.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter and Edmund Muskie') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease identify the image associated with the top caption that most closely represents \"President Carter and Edmund Muskie\" from the Images table.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The President appoints a Congress member avoiding the Ineligibility Clause') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the three paragraphs that are most relevant to the scenario where the President appoints a Congress member while avoiding the Ineligibility Clause, and provide their unique identifiers along with the articles they belong to and the similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The President appoints a Congress member avoiding the Ineligibility Clause') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the three paragraphs that are most relevant to the scenario where the President appoints a Congress member while avoiding the Ineligibility Clause, and provide their unique identifiers along with the articles they belong to and the similarity distance.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States Constitution') AS ref_vec_0\n\nSELECT a.title, a.url, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to find the top 3 paragraphs related to historical events in the United States Constitution from various articles. Please provide me with the titles and URLs of these articles, along with the position of each paragraph within its article.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States Constitution') AS ref_vec_0\n\nSELECT a.title, a.url, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nI want to find the top 3 paragraphs related to historical events in the United States Constitution from various articles. Please provide me with the titles and URLs of these articles, along with the position of each paragraph within its article.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States constitutional appointments and Saxbe fix') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nORDER BY rp.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Could you help me find the article title that's most related to \"United States constitutional appointments and Saxbe fix\"? I just need the top one, thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States constitutional appointments and Saxbe fix') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nORDER BY rp.distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you help me find the article title that's most related to \"United States constitutional appointments and Saxbe fix\"? I just need the top one, thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A mechanism by which the President of the United States appoints a current or former member of Congress') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title\nFROM ParagraphSearch ps\nJOIN Articles a ON toString(ps.article_id) = toString(a.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles of the 3 articles that most relate to the concept of how the President of the United States appoints a current or former member of Congress?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A mechanism by which the President of the United States appoints a current or former member of Congress') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title\nFROM ParagraphSearch ps\nJOIN Articles a ON toString(ps.article_id) = toString(a.article_id);" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the titles of the 3 articles that most relate to the concept of how the President of the United States appoints a current or former member of Congress?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'analysis on solar energy buildings') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url\nFROM Articles a\nJOIN ParagraphSearch ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 5 articles that include paragraphs most relevant to the topic of \"analysis on solar energy buildings\" and provide their titles and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'analysis on solar energy buildings') AS ref_vec_0,\n\nParagraphSearch AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url\nFROM Articles a\nJOIN ParagraphSearch ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the top 5 articles that include paragraphs most relevant to the topic of \"analysis on solar energy buildings\" and provide their titles and URLs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of climate change impacts and solutions') AS ref_vec_0\n\nSELECT \n a.title AS ArticleTitle, \n a.url AS ArticleURL, \n p.text AS ParagraphText, \n distance(p.text_embedding, ref_vec_0) AS ParagraphDistance\nFROM \n Paragraphs p\n JOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY ParagraphDistance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "What are the top 5 paragraphs related to the exploration of climate change impacts and solutions, including their article titles and URLs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of climate change impacts and solutions') AS ref_vec_0\n\nSELECT \n a.title AS ArticleTitle, \n a.url AS ArticleURL, \n p.text AS ParagraphText, \n distance(p.text_embedding, ref_vec_0) AS ParagraphDistance\nFROM \n Paragraphs p\n JOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY ParagraphDistance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat are the top 5 paragraphs related to the exploration of climate change impacts and solutions, including their article titles and URLs?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The story unfolds in the bustling city of New York, where characters navigate complex social dynamics.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the top 5 paragraphs related to the narrative of a story unfolding in New York City, focusing on complex social dynamics, and provide their paragraph IDs, associated article IDs, and text content.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The story unfolds in the bustling city of New York, where characters navigate complex social dynamics.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the top 5 paragraphs related to the narrative of a story unfolding in New York City, focusing on complex social dynamics, and provide their paragraph IDs, associated article IDs, and text content.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy electricity generation') AS ref_vec_0\n\nSELECT a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nWHERE a.wiki_id = 1\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few articles that are about generating electricity with solar energy?", + "external_knowledge": "The query utilizes a vector search mechanism where the `MATCH` operator performs an approximate nearest neighbor (ANN) search, which is used to find the articles most relevant to the phrase \"solar energy electricity generation.\" The `lembed` function with the specified model generates embeddings that capture the semantic meaning of the input text. The search returns the top 5 articles based on their closeness in vector space, indicating their conceptual similarity to the search term. In this context, \"a few\" refers to the limit of 5 articles. For the search to be effective, the embeddings are compared using the Euclidean distance, where a smaller distance indicates higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy electricity generation') AS ref_vec_0\n\nSELECT a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nWHERE a.wiki_id = 1\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe query utilizes a vector search mechanism where the `MATCH` operator performs an approximate nearest neighbor (ANN) search, which is used to find the articles most relevant to the phrase \"solar energy electricity generation.\" The `lembed` function with the specified model generates embeddings that capture the semantic meaning of the input text. The search returns the top 5 articles based on their closeness in vector space, indicating their conceptual similarity to the search term. In this context, \"a few\" refers to the limit of 5 articles. For the search to be effective, the embeddings are compared using the Euclidean distance, where a smaller distance indicates higher similarity.\nCan you find a few articles that are about generating electricity with solar energy?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of ethical conflicts in US Congress appointments') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you tell me which paragraph most closely relates to the exploration of ethical conflicts in US Congress appointments, based on the embeddings?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of ethical conflicts in US Congress appointments') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me which paragraph most closely relates to the exploration of ethical conflicts in US Congress appointments, based on the embeddings?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a solution for appointing members of Congress to civil offices.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT\n paragraph_id,\n article_id,\n text,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM\n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n a.title AS title\nFROM\n Articles a\nJOIN\n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY\n sp.distance AS distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Can you tell me the title of the article that most closely relates to the concept of \"The Saxbe fix as a solution for appointing members of Congress to civil offices,\" based on the top 5 most relevant paragraphs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a solution for appointing members of Congress to civil offices.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT\n paragraph_id,\n article_id,\n text,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM\n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT\n a.title AS title\nFROM\n Articles a\nJOIN\n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY\n sp.distance AS distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you tell me the title of the article that most closely relates to the concept of \"The Saxbe fix as a solution for appointing members of Congress to civil offices,\" based on the top 5 most relevant paragraphs?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbirdis a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Find the paragraph ID and article ID for the paragraph most related to \"To Kill a Mockingbird\" by Harper Lee.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbirdis a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nFind the paragraph ID and article ID for the paragraph most related to \"To Kill a Mockingbird\" by Harper Lee.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix mechanism allows Presidents to appoint current or former Congress members to civil office positions without constitutional restrictions') AS ref_vec_0\n\nSELECT a.article_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Which articles touch upon the mechanism that lets Presidents appoint Congress members to civil positions? Give me a handful of them.", + "external_knowledge": "In vector operations, the `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which helps find items that are most similar to a given concept based on vector embeddings. The `lembed()` function utilizes a specific vector model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`) to encode concepts into vector representations. The SQL query specifies `k=5`, meaning it retrieves the top 5 items that are most similar to the specified concept. This technique is useful for retrieving content that is contextually similar, as the vector comparison generally uses Euclidean distance where similarity increases as distance decreases.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix mechanism allows Presidents to appoint current or former Congress members to civil office positions without constitutional restrictions') AS ref_vec_0\n\nSELECT a.article_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn vector operations, the `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which helps find items that are most similar to a given concept based on vector embeddings. The `lembed()` function utilizes a specific vector model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`) to encode concepts into vector representations. The SQL query specifies `k=5`, meaning it retrieves the top 5 items that are most similar to the specified concept. This technique is useful for retrieving content that is contextually similar, as the vector comparison generally uses Euclidean distance where similarity increases as distance decreases.\nWhich articles touch upon the mechanism that lets Presidents appoint Congress members to civil positions? Give me a handful of them.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A timeless exploration of human resilience and courage.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n paragraph_id\nFROM \n RelevantParagraphs\nORDER BY \n distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the paragraph ID for the top paragraph that captures the essence of human resilience and courage? I'm curious to see which one stands out the most.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A timeless exploration of human resilience and courage.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT \n paragraph_id\nFROM \n RelevantParagraphs\nORDER BY \n distance;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the paragraph ID for the top paragraph that captures the essence of human resilience and courage? I'm curious to see which one stands out the most.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantArticles AS (\n SELECT article_id, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, a.article_id, p.distance\nFROM p_filtered AS p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY p.distance LIMIT 10;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Can you provide the paragraph IDs, article IDs, and their relevance distances for the 10 paragraphs most related to the concept of the \"Saxbe fix\" within the top 5 articles concerning the \"United States Constitution\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nRelevantArticles AS (\n SELECT article_id, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, a.article_id, p.distance\nFROM p_filtered AS p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY p.distance LIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the paragraph IDs, article IDs, and their relevance distances for the 10 paragraphs most related to the concept of the \"Saxbe fix\" within the top 5 articles concerning the \"United States Constitution\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of solar energy utilization in modern architecture') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you dig up the top 5 paragraphs that are all about using solar energy in modern buildings? I'd like to know their IDs and how closely they match the topic.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of solar energy utilization in modern architecture') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you dig up the top 5 paragraphs that are all about using solar energy in modern buildings? I'd like to know their IDs and how closely they match the topic.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring constitutional mechanisms to prevent ethical conflicts') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 2;", + "sql_result_column_count": 2, + "sql_result_rows_count": 2, + "sql_complexity": "Simple", + "question_style": "Vague", + "question": "**\n\nWhat are the IDs of the paragraphs in those couple of articles that delve into constitutional mechanisms for preventing ethical issues?\n\n**", + "external_knowledge": "**\n\nIn the context of vector searches using the `sqlite-lembed` extension, the `MATCH` operator facilitates approximate nearest neighbor searches. This means it identifies data points whose vector representations are closest to a specified reference vector, determined by a given textual concept. The `k=2` clause restricts the results to the two closest matches in terms of vector similarity, computed typically by Euclidean distance. The 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model is used to generate embeddings that capture semantic meaning, allowing for sophisticated querying based on conceptual similarity rather than direct keyword matching.\n\n**", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring constitutional mechanisms to prevent ethical conflicts') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 2;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n**\n\nIn the context of vector searches using the `sqlite-lembed` extension, the `MATCH` operator facilitates approximate nearest neighbor searches. This means it identifies data points whose vector representations are closest to a specified reference vector, determined by a given textual concept. The `k=2` clause restricts the results to the two closest matches in terms of vector similarity, computed typically by Euclidean distance. The 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model is used to generate embeddings that capture semantic meaning, allowing for sophisticated querying based on conceptual similarity rather than direct keyword matching.\n\n**\n**\n\nWhat are the IDs of the paragraphs in those couple of articles that delve into constitutional mechanisms for preventing ethical issues?\n\n**\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Taxation and government finance') AS ref_vec_0\n\nSELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Guide me on a journey to uncover the top 5 headings that resonate with the concept of 'Taxation and government finance,' revealing their unique identifiers and the closeness of their connection.", + "external_knowledge": "The query employs the \"MATCH\" operator to perform an approximate nearest neighbor (ANN) search, seeking headings that share a semantic closeness to the phrase \"Taxation and government finance.\" This operation uses vector embeddings to capture semantic meaning, with similarity measured by Euclidean distance (L2 norm). A lower distance indicates a higher degree of similarity. The model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' is designed to handle such tasks efficiently, translating textual concepts into vector space for comparison.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Taxation and government finance') AS ref_vec_0\n\nSELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe query employs the \"MATCH\" operator to perform an approximate nearest neighbor (ANN) search, seeking headings that share a semantic closeness to the phrase \"Taxation and government finance.\" This operation uses vector embeddings to capture semantic meaning, with similarity measured by Euclidean distance (L2 norm). A lower distance indicates a higher degree of similarity. The model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' is designed to handle such tasks efficiently, translating textual concepts into vector space for comparison.\nGuide me on a journey to uncover the top 5 headings that resonate with the concept of 'Taxation and government finance,' revealing their unique identifiers and the closeness of their connection.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous political figure') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A discussion on legislative procedures') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of historical events') AS ref_vec_2,\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_2) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT i.url\nFROM i_filtered AS i\nJOIN a_filtered AS a ON toString(i.article_id) = toString(a.article_id)\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY i.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Retrieve the URL of the image that best exemplifies a famous political figure, associated with an article on legislative procedures and a paragraph providing a detailed analysis of historical events, ensuring the selection is based on the highest relevance across these topics.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous political figure') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A discussion on legislative procedures') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of historical events') AS ref_vec_2,\n\ni_filtered AS (\n SELECT\n *,\n distance(caption_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\na_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_1) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_2) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT i.url\nFROM i_filtered AS i\nJOIN a_filtered AS a ON toString(i.article_id) = toString(a.article_id)\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY i.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nRetrieve the URL of the image that best exemplifies a famous political figure, associated with an article on legislative procedures and a paragraph providing a detailed analysis of historical events, ensuring the selection is based on the highest relevance across these topics.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Sixtus Muskie, U.S. Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Can you find me the image that best captures the essence of Edmund Sixtus Muskie as the U.S. Secretary of State? I just need the image ID, please.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Sixtus Muskie, U.S. Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you find me the image that best captures the essence of Edmund Sixtus Muskie as the U.S. Secretary of State? I just need the image ID, please.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Muskie, a prominent political figure in US history, served as Secretary of State.') AS ref_vec_0\n\nSELECT a.title, i.description, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 53, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Can you provide the titles and image descriptions for the top 5 articles that most pertinently cover the topic of Edmund Muskie, a significant political figure who served as Secretary of State in US history?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Edmund Muskie, a prominent political figure in US history, served as Secretary of State.') AS ref_vec_0\n\nSELECT a.title, i.description, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the titles and image descriptions for the top 5 articles that most pertinently cover the topic of Edmund Muskie, a significant political figure who served as Secretary of State in US history?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Modern architecture style') AS ref_vec_0\n\nSELECT a.article_id, a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the articles, including their IDs and titles, that have paragraphs best representing the Modern architecture style, considering the top 5 most relevant paragraphs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Modern architecture style') AS ref_vec_0\n\nSELECT a.article_id, a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the articles, including their IDs and titles, that have paragraphs best representing the Modern architecture style, considering the top 5 most relevant paragraphs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative green building designs in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Reveal the titles of articles that soar through the skyline of innovation, focusing on groundbreaking green building designs in the Windy City.", + "external_knowledge": "- The `MATCH` operator is used for performing an approximate nearest neighbor (ANN) search, which identifies the most similar items based on vector representation.\n- The `lembed` function leverages a pre-trained vector model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`) to encode textual content into a high-dimensional space, allowing for semantic similarity evaluation.\n- The parameter `p.k = 1` indicates that the query aims to find the single most relevant paragraph that aligns closely with the specified concept.\n- In vector operations, similarity is typically assessed via Euclidean distance (L2 norm), with smaller distances indicating greater similarity.\n- \"Innovative green building designs in Chicago\" refers to architectural advancements that prioritize sustainability and ecological considerations in Chicago.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative green building designs in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator is used for performing an approximate nearest neighbor (ANN) search, which identifies the most similar items based on vector representation.\n- The `lembed` function leverages a pre-trained vector model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`) to encode textual content into a high-dimensional space, allowing for semantic similarity evaluation.\n- The parameter `p.k = 1` indicates that the query aims to find the single most relevant paragraph that aligns closely with the specified concept.\n- In vector operations, similarity is typically assessed via Euclidean distance (L2 norm), with smaller distances indicating greater similarity.\n- \"Innovative green building designs in Chicago\" refers to architectural advancements that prioritize sustainability and ecological considerations in Chicago.\nReveal the titles of articles that soar through the skyline of innovation, focusing on groundbreaking green building designs in the Windy City.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the US Constitution''''s Ineligibility Clause') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Could you find a few paragraphs that have something to do with how the Saxbe fix relates to the Constitution, and let me know their content?", + "external_knowledge": "The query utilizes vector search, which involves converting textual data into numerical vectors using embeddings. The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, identifying items whose vector representations are closest to a target vector. Here, the CLIP model transforms the description of the Saxbe fix into a vector, and the search finds paragraphs with vectors most similar to this. The `k=5` limits the search to the five nearest items, based on Euclidean distance, where lower distances indicate greater similarity. This method allows for semantic matching beyond simple keyword matches.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the US Constitution''''s Ineligibility Clause') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe query utilizes vector search, which involves converting textual data into numerical vectors using embeddings. The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, identifying items whose vector representations are closest to a target vector. Here, the CLIP model transforms the description of the Saxbe fix into a vector, and the search finds paragraphs with vectors most similar to this. The `k=5` limits the search to the five nearest items, based on Euclidean distance, where lower distances indicate greater similarity. This method allows for semantic matching beyond simple keyword matches.\nCould you find a few paragraphs that have something to do with how the Saxbe fix relates to the Constitution, and let me know their content?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous historical figure delivering a speech at the United Nations') AS ref_vec_0\n\nSELECT image_id, url, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Metaphorical", + "question": "Uncover the trio of visual tales that capture the essence of a renowned historical luminary speaking at the global symposium of the United Nations.", + "external_knowledge": "In this context, the `MATCH` operator is utilized to perform an approximate nearest neighbor (ANN) search. The query seeks the top 3 images that are most semantically aligned with the input description vector \"A famous historical figure delivering a speech at the United Nations.\" The `lembed` function generates a vector representation using the `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` model, which is then used to compare against the existing description embeddings in the database. The similarity between vectors is determined based on the Euclidean distance, with closer distances indicating higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A famous historical figure delivering a speech at the United Nations') AS ref_vec_0\n\nSELECT image_id, url, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn this context, the `MATCH` operator is utilized to perform an approximate nearest neighbor (ANN) search. The query seeks the top 3 images that are most semantically aligned with the input description vector \"A famous historical figure delivering a speech at the United Nations.\" The `lembed` function generates a vector representation using the `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` model, which is then used to compare against the existing description embeddings in the database. The similarity between vectors is determined based on the Euclidean distance, with closer distances indicating higher similarity.\nUncover the trio of visual tales that capture the essence of a renowned historical luminary speaking at the global symposium of the United Nations.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of historical events and impacts') AS ref_vec_0\n\nSELECT a.article_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%significant development%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you list the 5 articles that most relate to the analysis of historical events and impacts, and include a significant development in their paragraphs?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of historical events and impacts') AS ref_vec_0\n\nSELECT a.article_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%significant development%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you list the 5 articles that most relate to the analysis of historical events and impacts, and include a significant development in their paragraphs?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States government') AS ref_vec_0\n\nSELECT a.title, p.text, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index < 5\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 25, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "I want to find the titles and content of the first five paragraphs from the top 5 articles that are most relevant to the \"History of the United States government\".", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States government') AS ref_vec_0\n\nSELECT a.title, p.text, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index < 5\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nI want to find the titles and content of the first five paragraphs from the top 5 articles that are most relevant to the \"History of the United States government\".\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park and solar energy in Chicago') AS ref_vec_0,\n\nArticleWikitextCTE AS (\n SELECT a.article_id, a.title, a.url, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM ArticleWikitextCTE\nJOIN Paragraphs p ON toString(ArticleWikitextCTE.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%Chicago%'\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 5 articles related to Millennium Park and solar energy in Chicago, and among those, find one that includes a paragraph mentioning Chicago. What is the title of that article?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park and solar energy in Chicago') AS ref_vec_0,\n\nArticleWikitextCTE AS (\n SELECT a.article_id, a.title, a.url, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles a\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM ArticleWikitextCTE\nJOIN Paragraphs p ON toString(ArticleWikitextCTE.article_id) = toString(p.article_id)\nWHERE p.text LIKE '%Chicago%'\nLIMIT 1;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the top 5 articles related to Millennium Park and solar energy in Chicago, and among those, find one that includes a paragraph mentioning Chicago. What is the title of that article?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional Convention and the Saxbe fix') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Could you provide the title of the article that is most relevant to the concept of \"Constitutional Convention and the Saxbe fix\"? Ensure that you find the top match based on similarity and return only one title.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional Convention and the Saxbe fix') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you provide the title of the article that is most relevant to the concept of \"Constitutional Convention and the Saxbe fix\"? Ensure that you find the top match based on similarity and return only one title.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Deep analysis of constitutional law and its historical implications') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you identify the titles of articles and the calculated similarity distances for the top 5 paragraphs that pertain to an in-depth study of constitutional law and its historical impacts?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Deep analysis of constitutional law and its historical implications') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you identify the titles of articles and the calculated similarity distances for the top 5 paragraphs that pertain to an in-depth study of constitutional law and its historical impacts?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A notable historical figure known for their diplomatic efforts during times of international tension') AS ref_vec_0\n\nSELECT a.title, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Articles a ON toString(i.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "Find the top article title related to a notable historical figure known for diplomatic efforts during international tension.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A notable historical figure known for their diplomatic efforts during times of international tension') AS ref_vec_0\n\nSELECT a.title, distance(i.caption_embedding, ref_vec_0) AS distance\nFROM Images i\nJOIN Articles a ON toString(i.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nFind the top article title related to a notable historical figure known for diplomatic efforts during international tension.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of modern architecture and green design in city structures') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few articles that delve into the modern and eco-friendly aspects of architecture within urban environments?", + "external_knowledge": "The \"MATCH\" operator in SQLite-vec performs an approximate nearest neighbor (ANN) search, allowing for efficient retrieval of items that are most similar to a given vector representation. The phrase \"modern architecture and green design in city structures\" is converted into a vector using the 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model, which captures semantic meaning and context. The query retrieves the top k=3 most similar items, using Euclidean distance as a measure of similarity. In practical terms, this query is identifying articles that are most related to themes of contemporary architecture and sustainable design in urban settings.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An exploration of modern architecture and green design in city structures') AS ref_vec_0\n\nSELECT a.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe \"MATCH\" operator in SQLite-vec performs an approximate nearest neighbor (ANN) search, allowing for efficient retrieval of items that are most similar to a given vector representation. The phrase \"modern architecture and green design in city structures\" is converted into a vector using the 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model, which captures semantic meaning and context. The query retrieves the top k=3 most similar items, using Euclidean distance as a measure of similarity. In practical terms, this query is identifying articles that are most related to themes of contemporary architecture and sustainable design in urban settings.\nCan you find a few articles that delve into the modern and eco-friendly aspects of architecture within urban environments?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring the constitutional dynamics and historical context of the Saxbe fix mechanism') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 paragraph IDs where the text dives into the constitutional dynamics and history around the Saxbe fix mechanism?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploring the constitutional dynamics and historical context of the Saxbe fix mechanism') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the top 5 paragraph IDs where the text dives into the constitutional dynamics and history around the Saxbe fix mechanism?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The influence of historical events on modern culture and society') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title\nFROM \n Articles a\nJOIN \n RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nWHERE \n a.title LIKE '%History%'\nORDER BY \n rp.distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 5 articles related to the influence of historical events on modern culture and society, focusing on those with \"History\" in their titles?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The influence of historical events on modern culture and society') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title\nFROM \n Articles a\nJOIN \n RelevantParagraphs rp ON toString(a.article_id) = toString(rp.article_id)\nWHERE \n a.title LIKE '%History%'\nORDER BY \n rp.distance;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the titles of the top 5 articles related to the influence of historical events on modern culture and society, focusing on those with \"History\" in their titles?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix is a legislative strategy to address the Ineligibility Clause') AS ref_vec_0,\n\nSimilarHeadings AS (\n SELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT heading_text\nFROM SimilarHeadings;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the top 5 headings related to the legislative strategy known as the Saxbe fix used to address the Ineligibility Clause?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix is a legislative strategy to address the Ineligibility Clause') AS ref_vec_0,\n\nSimilarHeadings AS (\n SELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT heading_text\nFROM SimilarHeadings;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the top 5 headings related to the legislative strategy known as the Saxbe fix used to address the Ineligibility Clause?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed explanation about the Saxbe fix and its implications.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the IDs and similarity distances for the top 5 paragraphs that most effectively explain the Saxbe fix and its implications?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed explanation about the Saxbe fix and its implications.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the IDs and similarity distances for the top 5 paragraphs that most effectively explain the Saxbe fix and its implications?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The impact of technology on modern education systems') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced technological devices in classrooms') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarParagraphs AS (\n SELECT\n p.paragraph_id AS paragraph_id,\n p.article_id AS article_id,\n p.paragraph_index AS paragraph_index,\n p.text AS text,\n p.distance AS paragraph_distance\n FROM p_filtered AS p\n ORDER BY\n paragraph_distance\n),\n\nRelatedImages AS (\n SELECT\n i.image_id AS image_id,\n i.article_id AS article_id,\n i.filename AS filename,\n i.image_title AS image_title,\n i.url AS url,\n i.distance AS image_distance\n FROM i_filtered AS i\n ORDER BY\n image_distance\n)\n\nSELECT\n sp.article_id AS article_id\nFROM\n SimilarParagraphs sp\nJOIN\n RelatedImages ri ON toString(sp.article_id) = toString(ri.article_id)\nORDER BY\n sp.paragraph_distance + ri.image_distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Vague", + "question": "Which article stands out for its insights on how technology reshapes classrooms and includes visuals of cutting-edge educational devices?", + "external_knowledge": "In this context, the vector operations involve the `MATCH` function, which performs an approximate nearest neighbor (ANN) search to find the closest matches for specified concepts. The `k=5` indicates the top 5 results are considered based on their vector proximity, determined by Euclidean distance. The embeddings used reflect the semantic meaning of phrases related to the impact of technology on education and advanced devices in classrooms, implying that paragraphs and images are selected based on how well they align with these themes.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The impact of technology on modern education systems') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Advanced technological devices in classrooms') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nSimilarParagraphs AS (\n SELECT\n p.paragraph_id AS paragraph_id,\n p.article_id AS article_id,\n p.paragraph_index AS paragraph_index,\n p.text AS text,\n p.distance AS paragraph_distance\n FROM p_filtered AS p\n ORDER BY\n paragraph_distance\n),\n\nRelatedImages AS (\n SELECT\n i.image_id AS image_id,\n i.article_id AS article_id,\n i.filename AS filename,\n i.image_title AS image_title,\n i.url AS url,\n i.distance AS image_distance\n FROM i_filtered AS i\n ORDER BY\n image_distance\n)\n\nSELECT\n sp.article_id AS article_id\nFROM\n SimilarParagraphs sp\nJOIN\n RelatedImages ri ON toString(sp.article_id) = toString(ri.article_id)\nORDER BY\n sp.paragraph_distance + ri.image_distance\nLIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn this context, the vector operations involve the `MATCH` function, which performs an approximate nearest neighbor (ANN) search to find the closest matches for specified concepts. The `k=5` indicates the top 5 results are considered based on their vector proximity, determined by Euclidean distance. The embeddings used reflect the semantic meaning of phrases related to the impact of technology on education and advanced devices in classrooms, implying that paragraphs and images are selected based on how well they align with these themes.\nWhich article stands out for its insights on how technology reshapes classrooms and includes visuals of cutting-edge educational devices?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and electricity generation in buildings') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticlesWithImages AS (\n SELECT a.article_id, a.title, i.image_id, i.filename, i.description\n FROM Articles a\n JOIN Images i ON toString(a.article_id) = toString(i.article_id)\n)\n\nSELECT a.title\nFROM ArticlesWithImages a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top article related to solar energy and electricity generation in buildings, which also includes images, and give me its title.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and electricity generation in buildings') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticlesWithImages AS (\n SELECT a.article_id, a.title, i.image_id, i.filename, i.description\n FROM Articles a\n JOIN Images i ON toString(a.article_id) = toString(i.article_id)\n)\n\nSELECT a.title\nFROM ArticlesWithImages a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the top article related to solar energy and electricity generation in buildings, which also includes images, and give me its title.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'an explanation about legislative mechanisms in the US') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM VectorSearchResults\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "What are the paragraph IDs for the top 5 paragraphs explaining legislative mechanisms in the US?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'an explanation about legislative mechanisms in the US') AS ref_vec_0,\n\nVectorSearchResults AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT paragraph_id\nFROM VectorSearchResults\nORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat are the paragraph IDs for the top 5 paragraphs explaining legislative mechanisms in the US?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of solar energy generation in urban architecture') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id,\n a.title AS title,\n a.url AS url,\n p.text AS text,\n distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Which five literary lanterns illuminate the synergy between solar power and cityscape creation within the architectural cosmos, shining through in articles with their titles and digital paths?", + "external_knowledge": "In this query, the `MATCH` operator is used to perform an approximate nearest neighbor search, comparing the text embeddings of paragraphs to the vector representation of the given query using a model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`). The `k = 5` specifies that we want the top 5 most relevant instances. Vectors, which represent semantic meaning, are compared using Euclidean distance; the lower the distance, the more similar the content. The query thus extracts paragraphs closely aligned with the thematic essence of solar energy within urban architecture.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of solar energy generation in urban architecture') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id,\n a.title AS title,\n a.url AS url,\n p.text AS text,\n distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn this query, the `MATCH` operator is used to perform an approximate nearest neighbor search, comparing the text embeddings of paragraphs to the vector representation of the given query using a model (`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`). The `k = 5` specifies that we want the top 5 most relevant instances. Vectors, which represent semantic meaning, are compared using Euclidean distance; the lower the distance, the more similar the content. The query thus extracts paragraphs closely aligned with the thematic essence of solar energy within urban architecture.\nWhich five literary lanterns illuminate the synergy between solar power and cityscape creation within the architectural cosmos, shining through in articles with their titles and digital paths?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles that are all about solar energy and architecture in Chicago? I'm just looking for their titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the top 5 articles that are all about solar energy and architecture in Chicago? I'm just looking for their titles.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you find the article that best represents the topic of solar energy and architecture in Chicago? I need the article's ID.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and architecture in Chicago') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you find the article that best represents the topic of solar energy and architecture in Chicago? I need the article's ID.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has subsequently become relevant for appointments by presidents of both parties') AS ref_vec_0,\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticleDetails AS (\n SELECT article_id, url\n FROM Articles\n)\n\nSELECT ad.url\nFROM ParagraphMatches pm\nJOIN ArticleDetails ad ON toString(pm.article_id) = toString(ad.article_id)\nORDER BY pm.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the URL of the most relevant article that discusses the Saxbe fix's importance in presidential appointments?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has subsequently become relevant for appointments by presidents of both parties') AS ref_vec_0,\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n),\n\nArticleDetails AS (\n SELECT article_id, url\n FROM Articles\n)\n\nSELECT ad.url\nFROM ParagraphMatches pm\nJOIN ArticleDetails ad ON toString(pm.article_id) = toString(ad.article_id)\nORDER BY pm.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the URL of the most relevant article that discusses the Saxbe fix's importance in presidential appointments?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy in modern architecture') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "Top 5 articles on generating electricity from solar energy in modern architecture. Return their IDs and titles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy in modern architecture') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nTop 5 articles on generating electricity from solar energy in modern architecture. Return their IDs and titles.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions.') AS ref_vec_0,\n\ntop_paragraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, tp.distance\nFROM top_paragraphs tp\nJOIN Articles a ON toString(tp.article_id) = toString(a.article_id)\nORDER BY tp.distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you find the article that has a paragraph most relevant to the idea of \"The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions\"? I'd love to know the title and URL of the article that comes out on top!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions.') AS ref_vec_0,\n\ntop_paragraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, tp.distance\nFROM top_paragraphs tp\nJOIN Articles a ON toString(tp.article_id) = toString(a.article_id)\nORDER BY tp.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you find the article that has a paragraph most relevant to the idea of \"The Saxbe fix is a legislative mechanism to circumvent constitutional restrictions\"? I'd love to know the title and URL of the article that comes out on top!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed exploration of a historical event') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the impact') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nArticleMatches AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.text\nFROM ArticleMatches am\nJOIN p_filtered AS p ON toString(am.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "(Natural Language Question capturing all query elements)", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed exploration of a historical event') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the impact') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nArticleMatches AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.text\nFROM ArticleMatches am\nJOIN p_filtered AS p ON toString(am.article_id) = toString(p.article_id)\nORDER BY p.distance\nLIMIT 1;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n(Natural Language Question capturing all query elements)\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of modern technology trends') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An image depicting advanced technology devices') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technology advancements') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_2) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title, a.url, distance \n FROM a_filtered AS a\n),\n\nSimilarImages AS (\n SELECT i.image_id, i.filename, i.url, i.article_id, distance\n FROM i_filtered AS i\n),\n\nRelatedHeadings AS (\n SELECT h.heading_id, h.heading_text\n FROM h_filtered AS h\n)\n\nSELECT sa.title\nFROM SimilarArticles sa\nJOIN SimilarImages si ON toString(sa.article_id) = toString(si.article_id)\nJOIN Image_Headings ih ON toString(si.image_id) = toString(ih.image_id)\nJOIN RelatedHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY sa.distance, si.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Could you please locate the article title that best aligns with a detailed study on modern technology trends, particularly those articles featuring images of advanced technology devices and related to headings on technology advancements? Just give me the top one, please!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Detailed analysis of modern technology trends') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An image depicting advanced technology devices') AS ref_vec_1,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technology advancements') AS ref_vec_2,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_2) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT a.article_id, a.title, a.url, distance \n FROM a_filtered AS a\n),\n\nSimilarImages AS (\n SELECT i.image_id, i.filename, i.url, i.article_id, distance\n FROM i_filtered AS i\n),\n\nRelatedHeadings AS (\n SELECT h.heading_id, h.heading_text\n FROM h_filtered AS h\n)\n\nSELECT sa.title\nFROM SimilarArticles sa\nJOIN SimilarImages si ON toString(sa.article_id) = toString(si.article_id)\nJOIN Image_Headings ih ON toString(si.image_id) = toString(ih.image_id)\nJOIN RelatedHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY sa.distance, si.distance\nLIMIT 1;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you please locate the article title that best aligns with a detailed study on modern technology trends, particularly those articles featuring images of advanced technology devices and related to headings on technology advancements? Just give me the top one, please!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and its impact on clean energy generation') AS ref_vec_0\n\nSELECT p.text, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 172, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Could you find a few paragraphs from the articles that discuss solar energy's role in promoting clean energy?", + "external_knowledge": "The `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search, which finds data points in a vector space that are closest to a given query vector. In this context, \"lembed\" refers to the embedding of a phrase using the specified model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'. The `k=5` parameter specifies that the query should return the top 5 articles most relevant to the topic of solar energy and its influence on clean energy generation. The similarity is measured using Euclidean distance (L2 norm), where a smaller distance implies higher similarity. This approach is frequently used in information retrieval to find text or documents related to a specific topic based on semantic similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy and its impact on clean energy generation') AS ref_vec_0\n\nSELECT p.text, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in the SQL query performs an approximate nearest neighbor (ANN) search, which finds data points in a vector space that are closest to a given query vector. In this context, \"lembed\" refers to the embedding of a phrase using the specified model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'. The `k=5` parameter specifies that the query should return the top 5 articles most relevant to the topic of solar energy and its influence on clean energy generation. The similarity is measured using Euclidean distance (L2 norm), where a smaller distance implies higher similarity. This approach is frequently used in information retrieval to find text or documents related to a specific topic based on semantic similarity.\nCould you find a few paragraphs from the articles that discuss solar energy's role in promoting clean energy?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of events and occurrences in a specific context') AS ref_vec_0\n\nSELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 3, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the heading that best describes the history of events and occurrences in a specific context?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of events and occurrences in a specific context') AS ref_vec_0\n\nSELECT heading_id, heading_text, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\nFROM Headings\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the heading that best describes the history of events and occurrences in a specific context?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison proposed language at the Constitutional Convention that was adopted as the Ineligibility Clause after debate.') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url,\n distance(p.text_embedding, ref_vec_0) AS distance \nFROM \n Articles a\nJOIN \n Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 4, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Top 3 articles related to James Madison's proposal at the Constitutional Convention. List their IDs, titles, and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison proposed language at the Constitutional Convention that was adopted as the Ineligibility Clause after debate.') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url,\n distance(p.text_embedding, ref_vec_0) AS distance \nFROM \n Articles a\nJOIN \n Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nTop 3 articles related to James Madison's proposal at the Constitutional Convention. List their IDs, titles, and URLs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix in U.S. politics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, p.text, a.title, a.url\nFROM Paragraphs p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY a.distance, p.paragraph_index\nLIMIT 10;", + "sql_result_column_count": 4, + "sql_result_rows_count": 10, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to find paragraphs from articles that are topically relevant to \"The Saxbe fix in U.S. politics\". Please provide the IDs and text of the first 10 paragraphs from the top 5 articles, including the articles' titles and URLs. Ensure the paragraphs are sorted based on the articles' relevance and within the articles themselves.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix in U.S. politics') AS ref_vec_0,\n\nRelevantArticles AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, p.text, a.title, a.url\nFROM Paragraphs p\nJOIN RelevantArticles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY a.distance, p.paragraph_index\nLIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nI need to find paragraphs from articles that are topically relevant to \"The Saxbe fix in U.S. politics\". Please provide the IDs and text of the first 10 paragraphs from the top 5 articles, including the articles' titles and URLs. Ensure the paragraphs are sorted based on the articles' relevance and within the articles themselves.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History') AS ref_vec_0\n\nSELECT a.title, distance(h.heading_text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the titles of the top 3 articles that are most related to the topic of History based on their headings?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History') AS ref_vec_0\n\nSELECT a.title, distance(h.heading_text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the titles of the top 3 articles that are most related to the topic of History based on their headings?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the evolution of modern architecture across the globe') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you find the paragraph that best captures the theme of evolving modern architecture worldwide and share its content with me?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful analysis of the evolution of modern architecture across the globe') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you find the paragraph that best captures the theme of evolving modern architecture worldwide and share its content with me?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A historical image of an important political figure.') AS ref_vec_0\n\nSELECT image_id, description, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 images that showcase historical figures in politics, and tell me their descriptions?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A historical image of an important political figure.') AS ref_vec_0\n\nSELECT image_id, description, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the top 5 images that showcase historical figures in politics, and tell me their descriptions?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Key highlights and updates on modern architecture in urban spaces.') AS ref_vec_0\n\nSELECT article_id, wiki_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please find the top 5 articles that provide key highlights and updates on modern architecture in urban spaces? I need to know their IDs, titles, and where I can access them.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Key highlights and updates on modern architecture in urban spaces.') AS ref_vec_0\n\nSELECT article_id, wiki_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you please find the top 5 articles that provide key highlights and updates on modern architecture in urban spaces? I need to know their IDs, titles, and where I can access them.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion of legislative branch mechanisms exemplified by the Saxbe fix') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What is the ID and similarity distance of the top article related to the discussion of legislative branch mechanisms, specifically the Saxbe fix?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion of legislative branch mechanisms exemplified by the Saxbe fix') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat is the ID and similarity distance of the top article related to the discussion of legislative branch mechanisms, specifically the Saxbe fix?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States Senate') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 5, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Can you provide the paragraph IDs, article IDs, their index positions, and the paragraph text for the top 5 paragraphs that are most related to the \"History of the United States Senate\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'History of the United States Senate') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you provide the paragraph IDs, article IDs, their index positions, and the paragraph text for the top 5 paragraphs that are most related to the \"History of the United States Senate\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The significance of the Saxbe fix in U.S. appointments and its constitutional implications') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "What’s one notable piece about how the Saxbe fix impacts U.S. appointments and its constitutional aspects?", + "external_knowledge": "The \"MATCH\" operator in this context is used to perform an approximate nearest neighbor (ANN) search, which is a type of vector search that identifies items most similar to a given query based on vector embeddings. The function 'lembed' is part of the sqlite-vec extension and is used to generate these vector embeddings from the specified text model ('laion/CLIP-ViT-B-32-laion2B-s34B-b79K'). The vector search ranks entries by how closely they match the input concept, limiting the results to the top match ('LIMIT 1'). The use of vector embeddings allows for semantic similarity comparisons, meaning paragraphs are evaluated not just on keyword matching but on overall thematic and contextual similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The significance of the Saxbe fix in U.S. appointments and its constitutional implications') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe \"MATCH\" operator in this context is used to perform an approximate nearest neighbor (ANN) search, which is a type of vector search that identifies items most similar to a given query based on vector embeddings. The function 'lembed' is part of the sqlite-vec extension and is used to generate these vector embeddings from the specified text model ('laion/CLIP-ViT-B-32-laion2B-s34B-b79K'). The vector search ranks entries by how closely they match the input concept, limiting the results to the top match ('LIMIT 1'). The use of vector embeddings allows for semantic similarity comparisons, meaning paragraphs are evaluated not just on keyword matching but on overall thematic and contextual similarity.\nWhat’s one notable piece about how the Saxbe fix impacts U.S. appointments and its constitutional aspects?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix relates to legislative actions and appointments.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT paragraph_id\nFROM SimilarParagraphs;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "What is the paragraph ID for the paragraph most related to \"The Saxbe fix relates to legislative actions and appointments\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix relates to legislative actions and appointments.') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT paragraph_id\nFROM SimilarParagraphs;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat is the paragraph ID for the paragraph most related to \"The Saxbe fix relates to legislative actions and appointments\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Sustainable energy buildings in urban environments') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Metaphorical", + "question": "In the bustling cityscape where innovation meets ecology, which are the three leading articles illuminating the path of sustainable energy buildings nestled within urban jungles? Please uncover their titles and doorways to enlightenment.", + "external_knowledge": "The `MATCH` operator in vector searches performs an approximate nearest neighbor (ANN) search to find items most similar to a given concept. The `lembed` function evaluates embeddings, which are vector representations of concepts, against stored data. In this instance, \"Sustainable energy buildings in urban environments\" is the concept being explored using embeddings from the `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` model, which is trained to understand visual and textual content semantically. The query asks for `k=3`, meaning it retrieves the top three articles with the smallest Euclidean distance (L2 norm) from the search concept, indicating the highest similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Sustainable energy buildings in urban environments') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in vector searches performs an approximate nearest neighbor (ANN) search to find items most similar to a given concept. The `lembed` function evaluates embeddings, which are vector representations of concepts, against stored data. In this instance, \"Sustainable energy buildings in urban environments\" is the concept being explored using embeddings from the `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` model, which is trained to understand visual and textual content semantically. The query asks for `k=3`, meaning it retrieves the top three articles with the smallest Euclidean distance (L2 norm) from the search concept, indicating the highest similarity.\nIn the bustling cityscape where innovation meets ecology, which are the three leading articles illuminating the path of sustainable energy buildings nestled within urban jungles? Please uncover their titles and doorways to enlightenment.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Wikipedia article about legislative processes') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url, \n i.filename AS filename, \n i.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 5, + "sql_result_rows_count": 76, + "sql_complexity": "Moderate", + "question_style": "Formal", + "question": "Identify the 10 articles most relevant to the topic of legislative processes as found on Wikipedia, and provide their titles, URLs, and associated image filenames and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Wikipedia article about legislative processes') AS ref_vec_0\n\nSELECT \n a.article_id AS article_id, \n a.title AS title, \n a.url AS url, \n i.filename AS filename, \n i.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the 10 articles most relevant to the topic of legislative processes as found on Wikipedia, and provide their titles, URLs, and associated image filenames and URLs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A deep exploration of renewable energy sources and their impact on urban infrastructure') AS ref_vec_0,\n\nParagraphMatch AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE paragraph_index BETWEEN 1 AND 10\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, a.title, i.image_title\nFROM ParagraphMatch p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY p.distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "Please find the three most relevant paragraphs about \"A deep exploration of renewable energy sources and their impact on urban infrastructure\", and provide their IDs, along with the titles of the articles and associated images. Ensure the paragraphs are from indices 1 to 10.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A deep exploration of renewable energy sources and their impact on urban infrastructure') AS ref_vec_0,\n\nParagraphMatch AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n WHERE paragraph_index BETWEEN 1 AND 10\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT p.paragraph_id, a.title, i.image_title\nFROM ParagraphMatch p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY p.distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the three most relevant paragraphs about \"A deep exploration of renewable energy sources and their impact on urban infrastructure\", and provide their IDs, along with the titles of the articles and associated images. Ensure the paragraphs are from indices 1 to 10.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Significant historical events shaping our world') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%History%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM FilteredArticles fa\nJOIN Paragraphs p ON toString(fa.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "Can you find a few paragraphs from articles on history that delve into major events that have shaped our world? Just send me their IDs.", + "external_knowledge": "In vector search operations, the \"MATCH\" operator performs an approximate nearest neighbor (ANN) search, which identifies items in the dataset that are closest in vector space to a specified query vector. This is often used to find semantically similar items. The parameter \"k = 5\" specifies that the top five matches should be returned. Euclidean distance is commonly used as the measure of similarity, meaning items with smaller distances are considered more similar to the query. The embedding model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is utilized to convert text into vectors, allowing the query to identify paragraphs most related to the concept of \"Significant historical events shaping our world.\"", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Significant historical events shaping our world') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%History%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM FilteredArticles fa\nJOIN Paragraphs p ON toString(fa.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn vector search operations, the \"MATCH\" operator performs an approximate nearest neighbor (ANN) search, which identifies items in the dataset that are closest in vector space to a specified query vector. This is often used to find semantically similar items. The parameter \"k = 5\" specifies that the top five matches should be returned. Euclidean distance is commonly used as the measure of similarity, meaning items with smaller distances are considered more similar to the query. The embedding model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is utilized to convert text into vectors, allowing the query to identify paragraphs most related to the concept of \"Significant historical events shaping our world.\"\nCan you find a few paragraphs from articles on history that delve into major events that have shaped our world? Just send me their IDs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Congress and legislative processes') AS ref_vec_0,\n\nArticleVectorSearch AS (\n SELECT \n article_id, \n title, \n distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ArticleVectorSearch.title AS title, \n Paragraphs.text AS text\nFROM ArticleVectorSearch\nJOIN Paragraphs ON toString(ArticleVectorSearch.article_id) = toString(Paragraphs.article_id)\nWHERE Paragraphs.paragraph_index = 0\nORDER BY ArticleVectorSearch.distance;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 5 articles about the United States Congress and legislative processes, and provide their titles along with the text of the first paragraph. Make sure to order them starting with the most relevant ones!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'United States Congress and legislative processes') AS ref_vec_0,\n\nArticleVectorSearch AS (\n SELECT \n article_id, \n title, \n distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n ArticleVectorSearch.title AS title, \n Paragraphs.text AS text\nFROM ArticleVectorSearch\nJOIN Paragraphs ON toString(ArticleVectorSearch.article_id) = toString(Paragraphs.article_id)\nWHERE Paragraphs.paragraph_index = 0\nORDER BY ArticleVectorSearch.distance;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the top 5 articles about the United States Congress and legislative processes, and provide their titles along with the text of the first paragraph. Make sure to order them starting with the most relevant ones!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism that allows the President to appoint members of Congress to civil office without constitutional restrictions.') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a few articles that discuss mechanisms like the Saxbe fix related to presidential appointments?", + "external_knowledge": "- **Vector Operations**: The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which identifies items that are similar to a given query based on vector embeddings.\n- **KNN Queries**: The parameter `k = 5` specifies that the search should return the top 5 most similar articles.\n- **Model and Embeddings**: The embedding model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' converts text into a vector format that captures semantic meaning, allowing for comparison based on content similarity.\n- **Domain Context**: The Saxbe fix is a legislative mechanism that addresses constitutional barriers regarding presidential appointments from Congress.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism that allows the President to appoint members of Congress to civil office without constitutional restrictions.') AS ref_vec_0\n\nSELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n- **Vector Operations**: The `MATCH` operator is used to perform an approximate nearest neighbor (ANN) search, which identifies items that are similar to a given query based on vector embeddings.\n- **KNN Queries**: The parameter `k = 5` specifies that the search should return the top 5 most similar articles.\n- **Model and Embeddings**: The embedding model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' converts text into a vector format that captures semantic meaning, allowing for comparison based on content similarity.\n- **Domain Context**: The Saxbe fix is a legislative mechanism that addresses constitutional barriers regarding presidential appointments from Congress.\nCan you find a few articles that discuss mechanisms like the Saxbe fix related to presidential appointments?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of legislative procedures in government') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance \nFROM Articles\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Concise", + "question": "What are the top 5 articles related to the analysis of legislative procedures in government?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Analysis of legislative procedures in government') AS ref_vec_0\n\nSELECT article_id, title, url, distance(Articles.raw_html_embedding, ref_vec_0) AS distance \nFROM Articles\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat are the top 5 articles related to the analysis of legislative procedures in government?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy architecture feature') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Energy efficient pavilion view') AS ref_vec_1,\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelatedHeadings AS (\n SELECT h.heading_text\n FROM h_filtered AS h\n JOIN Image_Headings ih ON toString(h.heading_id) = toString(ih.heading_id)\n)\n\nSELECT i.description\nFROM i_filtered AS i;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "What are the descriptions of the top five images related to an energy-saving pavilion perspective?", + "external_knowledge": "- The `MATCH` operator is employed for approximate nearest neighbor (ANN) search, used here to find items that are semantically similar based on vector embeddings.\n- The `k=3` and `k=5` parameters specify that the query should return the top 3 headings and top 5 images that best match the given semantic descriptions, respectively.\n- The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is used, which is known for understanding the visual-textual relationship and is effective for tasks involving image and text embeddings.\n- In such vector searches, similarity is measured by the proximity of vector embeddings, typically using a Euclidean distance metric where a smaller distance indicates higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy architecture feature') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Energy efficient pavilion view') AS ref_vec_1,\n\nh_filtered AS (\n SELECT\n *,\n distance(heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n\n ORDER BY distance\n LIMIT 3\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelatedHeadings AS (\n SELECT h.heading_text\n FROM h_filtered AS h\n JOIN Image_Headings ih ON toString(h.heading_id) = toString(ih.heading_id)\n)\n\nSELECT i.description\nFROM i_filtered AS i;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator is employed for approximate nearest neighbor (ANN) search, used here to find items that are semantically similar based on vector embeddings.\n- The `k=3` and `k=5` parameters specify that the query should return the top 3 headings and top 5 images that best match the given semantic descriptions, respectively.\n- The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is used, which is known for understanding the visual-textual relationship and is effective for tasks involving image and text embeddings.\n- In such vector searches, similarity is measured by the proximity of vector embeddings, typically using a Euclidean distance metric where a smaller distance indicates higher similarity.\nWhat are the descriptions of the top five images related to an energy-saving pavilion perspective?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id,\n article_id,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT \n a.title AS title \nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance LIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey! Can you snag the article title that has a paragraph really close to the topic of \"Harper Lee’s To Kill a Mockingbird\"? I'm looking for the most spot-on match!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id,\n article_id,\n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT \n a.title AS title \nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance LIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you snag the article title that has a paragraph really close to the topic of \"Harper Lee’s To Kill a Mockingbird\"? I'm looking for the most spot-on match!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in US constitutional law') AS ref_vec_0\n\nSELECT a.title, i.filename, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 35, + "sql_complexity": "Moderate", + "question_style": "Interrogative", + "question": "Could you show me the top 5 articles related to \"The Saxbe fix and its implications in US constitutional law\" along with the filenames of their associated images?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in US constitutional law') AS ref_vec_0\n\nSELECT a.title, i.filename, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the top 5 articles related to \"The Saxbe fix and its implications in US constitutional law\" along with the filenames of their associated images?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Congressional payment scheme') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelevantArticles AS (\n SELECT a.article_id, a.title\n FROM Articles a\n JOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\n),\n\nMatchingImages AS (\n SELECT i.article_id\n FROM i_filtered AS i\n)\n\nSELECT ra.title\nFROM RelevantArticles ra\nJOIN MatchingImages mi ON toString(ra.article_id) = toString(mi.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 2, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I want to find the titles of articles that include paragraphs highly related to the \"Congressional payment scheme\" and simultaneously contain images described as \"Portrait of Senator Edward Oliver Wolcott,\" selecting the top 5 matching articles based on both text and image descriptions.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Congressional payment scheme') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_1,\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 5\n),\n\nRelevantArticles AS (\n SELECT a.article_id, a.title\n FROM Articles a\n JOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\n),\n\nMatchingImages AS (\n SELECT i.article_id\n FROM i_filtered AS i\n)\n\nSELECT ra.title\nFROM RelevantArticles ra\nJOIN MatchingImages mi ON toString(ra.article_id) = toString(mi.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nI want to find the titles of articles that include paragraphs highly related to the \"Congressional payment scheme\" and simultaneously contain images described as \"Portrait of Senator Edward Oliver Wolcott,\" selecting the top 5 matching articles based on both text and image descriptions.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0,\n\nArticleCTE AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%solar energy%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN ArticleCTE a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the paragraph ID of the most relevant paragraph discussing \"Electricity generation from solar energy\" within articles whose titles include \"solar energy.\"", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Electricity generation from solar energy') AS ref_vec_0,\n\nArticleCTE AS (\n SELECT article_id, title\n FROM Articles\n WHERE title LIKE '%solar energy%'\n)\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN ArticleCTE a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the paragraph ID of the most relevant paragraph discussing \"Electricity generation from solar energy\" within articles whose titles include \"solar energy.\"\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism to avoid constitutional restrictions') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM SimilarArticles\nWHERE distance < 0.5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Vague", + "question": "What are some of the leading articles that might explore ways to bypass constitutional limitations?", + "external_knowledge": "In vector operations:\n- The `MATCH` operator is used to conduct an approximate nearest neighbor search, which is a method of finding the most similar items in terms of their vector representation.\n- The `k=5` parameter indicates the retrieval of the top 5 articles based on similarity from the embedding space defined by the vector model.\n- Similarity is assessed through Euclidean distance, where a smaller distance signifies higher similarity.\n- The phrase \"Mechanism to avoid constitutional restrictions\" serves as the conceptual target for the vector search, aiming to capture articles that are semantically aligned with this idea.\n- The threshold of `distance < 0.5` ensures that the articles are not only among the top five similar but also have a significant degree of relevance to this concept.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism to avoid constitutional restrictions') AS ref_vec_0,\n\nSimilarArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT title\nFROM SimilarArticles\nWHERE distance < 0.5;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn vector operations:\n- The `MATCH` operator is used to conduct an approximate nearest neighbor search, which is a method of finding the most similar items in terms of their vector representation.\n- The `k=5` parameter indicates the retrieval of the top 5 articles based on similarity from the embedding space defined by the vector model.\n- Similarity is assessed through Euclidean distance, where a smaller distance signifies higher similarity.\n- The phrase \"Mechanism to avoid constitutional restrictions\" serves as the conceptual target for the vector search, aiming to capture articles that are semantically aligned with this idea.\n- The threshold of `distance < 0.5` ensures that the articles are not only among the top five similar but also have a significant degree of relevance to this concept.\nWhat are some of the leading articles that might explore ways to bypass constitutional limitations?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Influential historical narrative') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, p.text, p.article_id\nFROM p_filtered AS p\nJOIN SimilarArticles sa ON toString(p.article_id) = toString(sa.article_id);", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Imperative", + "question": "**\n\nCould you please find the top 5 articles that are related to historical events in the United States and then identify the paragraphs within those articles that align with an influential historical narrative? Make sure to include the paragraph IDs, the text of the paragraphs, and the article IDs for only those paragraphs that meet the specific condition of k being 3.\n\n**", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events in the United States') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Influential historical narrative') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance\n FROM Articles_filtered AS Articles\n)\n\nSELECT p.paragraph_id, p.text, p.article_id\nFROM p_filtered AS p\nJOIN SimilarArticles sa ON toString(p.article_id) = toString(sa.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n**\n\nCould you please find the top 5 articles that are related to historical events in the United States and then identify the paragraphs within those articles that align with an influential historical narrative? Make sure to include the paragraph IDs, the text of the paragraphs, and the article IDs for only those paragraphs that meet the specific condition of k being 3.\n\n**\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An in-depth analysis of environmental impact and sustainable architecture.') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.raw_html LIKE '%Exelon%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Interrogative", + "question": "Could you tell me the IDs of the top 5 paragraphs that discuss an in-depth analysis of environmental impact and sustainable architecture within articles mentioning \"Exelon\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An in-depth analysis of environmental impact and sustainable architecture.') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.raw_html LIKE '%Exelon%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you tell me the IDs of the top 5 paragraphs that discuss an in-depth analysis of environmental impact and sustainable architecture within articles mentioning \"Exelon\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of modern web development techniques') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Illustration of advanced programming concepts') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nTopArticles AS (\n SELECT article_id, title\n FROM Articles_filtered AS Articles\n)\n\nSELECT i.filename\nFROM i_filtered AS i\nJOIN TopArticles ta ON toString(i.article_id) = toString(ta.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the realm where bytes and pixels dance, can you unveil the files of those images that picture advanced programming concepts with a touch of magic, tied to the tales of modern web sorcery?", + "external_knowledge": "- The `MATCH` operator in the context of vector databases performs an approximate nearest neighbor (ANN) search, which efficiently finds entries that are semantically similar based on the provided embedding.\n- The term \"lembed\" refers to a function that uses a pre-trained language model to generate embeddings for text, capturing its semantic meaning.\n- The models, such as `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, are designed to map textual descriptions to a high-dimensional space where similar concepts are located closer together.\n- The clause `LIMIT 5` in the vector search indicates retrieving the top 5 most relevant articles, emphasizing a selection of articles that best fit the specified theme.\n- L2 norm (Euclidean distance) is generally used to compute the closeness between vectors, meaning that shorter distances imply a higher degree of similarity.\n- The condition `i.k = 3` implies a specific constraint or attribute of the images which might be domain-specific (e.g., an identifier or a category).", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of modern web development techniques') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Illustration of advanced programming concepts') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_1) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 3\n),\n\nTopArticles AS (\n SELECT article_id, title\n FROM Articles_filtered AS Articles\n)\n\nSELECT i.filename\nFROM i_filtered AS i\nJOIN TopArticles ta ON toString(i.article_id) = toString(ta.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\n- The `MATCH` operator in the context of vector databases performs an approximate nearest neighbor (ANN) search, which efficiently finds entries that are semantically similar based on the provided embedding.\n- The term \"lembed\" refers to a function that uses a pre-trained language model to generate embeddings for text, capturing its semantic meaning.\n- The models, such as `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, are designed to map textual descriptions to a high-dimensional space where similar concepts are located closer together.\n- The clause `LIMIT 5` in the vector search indicates retrieving the top 5 most relevant articles, emphasizing a selection of articles that best fit the specified theme.\n- L2 norm (Euclidean distance) is generally used to compute the closeness between vectors, meaning that shorter distances imply a higher degree of similarity.\n- The condition `i.k = 3` implies a specific constraint or attribute of the images which might be domain-specific (e.g., an identifier or a category).\nIn the realm where bytes and pixels dance, can you unveil the files of those images that picture advanced programming concepts with a touch of magic, tied to the tales of modern web sorcery?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Without regard to the constitutional issue') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 1\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT i.image_title\nFROM i_filtered AS i\nJOIN p_filtered AS p ON toString(i.article_id) = toString(p.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Colloquial", + "question": "Hey! Could you help me track down the image titles for the best match images that are described as \"Portrait of Senator Edward Oliver Wolcott\" and belong to articles containing paragraphs related to \"Without regard to the constitutional issue\"? Thanks a bunch!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Portrait of Senator Edward Oliver Wolcott') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Without regard to the constitutional issue') AS ref_vec_1,\n\ni_filtered AS (\n SELECT\n *,\n distance(description_embedding, ref_vec_0) AS distance\n FROM Images\n\n ORDER BY distance\n LIMIT 1\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT i.image_title\nFROM i_filtered AS i\nJOIN p_filtered AS p ON toString(i.article_id) = toString(p.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you help me track down the image titles for the best match images that are described as \"Portrait of Senator Edward Oliver Wolcott\" and belong to articles containing paragraphs related to \"Without regard to the constitutional issue\"? Thanks a bunch!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical overview of U.S. constitutional law') AS ref_vec_0\n\nSELECT a.article_id, a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Senate%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Please find the top 5 articles that provide a historical overview of U.S. constitutional law and include images with descriptions mentioning the Senate. Return their IDs, titles, and URLs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical overview of U.S. constitutional law') AS ref_vec_0\n\nSELECT a.article_id, a.title, a.url, distance(a.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Images i ON toString(a.article_id) = toString(i.article_id)\nWHERE i.description LIKE '%Senate%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the top 5 articles that provide a historical overview of U.S. constitutional law and include images with descriptions mentioning the Senate. Return their IDs, titles, and URLs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park solar energy structures') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy generation in Chicago') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance \n FROM Articles_filtered AS Articles\n)\n\nSELECT sa.article_id, p.paragraph_id\nFROM SimilarArticles sa\nJOIN p_filtered AS p ON toString(sa.article_id) = toString(p.article_id);", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Amidst the architectural marvels of Millennium Park, what are the top 5 articles that weave tales of its solar energy structures? And within these narratives, can you uncover the top 3 passages that echo the harmonious symphony of solar energy generation in the heart of Chicago?", + "external_knowledge": "In vector-based searches like those used in this query, the `MATCH` operator performs an approximate nearest neighbor search, tapping into the power of vector embeddings to find closely related items based on their semantic meanings. Here, the `lembed` function utilizes the \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" model to transform text inputs into embeddings that capture their semantic essence. The parameter `k` specifies the number of top similar items to return, with `k = 5` for articles and `k = 3` for paragraphs in this query. The similarity between vectors is typically measured using Euclidean distance, where a smaller distance indicates a stronger similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Millennium Park solar energy structures') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'solar energy generation in Chicago') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 3\n),\n\nSimilarArticles AS (\n SELECT article_id, title, distance \n FROM Articles_filtered AS Articles\n)\n\nSELECT sa.article_id, p.paragraph_id\nFROM SimilarArticles sa\nJOIN p_filtered AS p ON toString(sa.article_id) = toString(p.article_id);" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn vector-based searches like those used in this query, the `MATCH` operator performs an approximate nearest neighbor search, tapping into the power of vector embeddings to find closely related items based on their semantic meanings. Here, the `lembed` function utilizes the \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" model to transform text inputs into embeddings that capture their semantic essence. The parameter `k` specifies the number of top similar items to return, with `k = 5` for articles and `k = 3` for paragraphs in this query. The similarity between vectors is typically measured using Euclidean distance, where a smaller distance indicates a stronger similarity.\nAmidst the architectural marvels of Millennium Park, what are the top 5 articles that weave tales of its solar energy structures? And within these narratives, can you uncover the top 3 passages that echo the harmonious symphony of solar energy generation in the heart of Chicago?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, a.title, a.url, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 17818377\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 6, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Descriptive", + "question": "Please provide the paragraph ID, article ID, text, title, URL, and similarity distance for the top 5 paragraphs related to \"Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch,\" specifically from articles that are associated with the Wikipedia ID 17818377.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT p.paragraph_id, p.article_id, p.text, a.title, a.url, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.wiki_id = 17818377\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease provide the paragraph ID, article ID, text, title, URL, and similarity distance for the top 5 paragraphs related to \"Exploration of racial injustice and moral growth seen through the innocent yet perceptive eyes of Scout Finch,\" specifically from articles that are associated with the Wikipedia ID 17818377.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful paragraph about constitutional laws and their implications on public office appointments') AS ref_vec_0\n\nSELECT a.title, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Highly Complex", + "question_style": "Formal", + "question": "Identify the titles and paragraph indices of the top 10 paragraphs related to constitutional laws and their implications on public office appointments, as found in the articles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'An insightful paragraph about constitutional laws and their implications on public office appointments') AS ref_vec_0\n\nSELECT a.title, p.paragraph_index, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the titles and paragraph indices of the top 10 paragraphs related to constitutional laws and their implications on public office appointments, as found in the articles.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on ethical conflicts in the government') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Ineligibility Clause%'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Please identify the top 5 paragraphs that discuss ethical conflicts in the government, ensuring they are from articles with the title \"Ineligibility Clause\". I need their paragraph IDs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on ethical conflicts in the government') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Ineligibility Clause%'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease identify the top 5 paragraphs that discuss ethical conflicts in the government, ensuring they are from articles with the title \"Ineligibility Clause\". I need their paragraph IDs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison and ethical conflicts in political appointments') AS ref_vec_0,\n\nRelatedParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelatedParagraphs;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Descriptive", + "question": "I need to find the top 5 paragraphs that discuss James Madison and ethical conflicts in political appointments. Please provide their textual content, sorted by relevance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'James Madison and ethical conflicts in political appointments') AS ref_vec_0,\n\nRelatedParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelatedParagraphs;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nI need to find the top 5 paragraphs that discuss James Madison and ethical conflicts in political appointments. Please provide their textual content, sorted by relevance.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy projects in urban areas') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nWHERE h.heading_text = 'Background'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the titles of the top 5 articles discussing the background on renewable energy projects in urban areas?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Renewable energy projects in urban areas') AS ref_vec_0\n\nSELECT a.title, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Headings h ON toString(a.article_id) = toString(h.heading_id)\nWHERE h.heading_text = 'Background'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat are the titles of the top 5 articles discussing the background on renewable energy projects in urban areas?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events') AS ref_vec_0,\n\nRelevantHeadings AS (\n SELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.description\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN RelevantHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY rh.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "What is the description of the image that paints the clearest picture of historical events?", + "external_knowledge": "In the realm of vector operations, the `MATCH` operator is used for performing approximate nearest neighbor (ANN) searches, which are efficient for finding items similar to a given concept. The `lembed` function generates a vector representation of the text \"Historical events,\" which allows the system to capture semantic meanings. The `k = 5` clause indicates the query is interested in the top 5 closest matches based on Euclidean distance, with smaller distances indicating higher similarity. The approach leverages embeddings from the model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', which is fine-tuned for understanding visual and textual content.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Historical events') AS ref_vec_0,\n\nRelevantHeadings AS (\n SELECT heading_id, distance(Headings.heading_text_embedding, ref_vec_0) AS distance\n FROM Headings\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT i.description\nFROM Images i\nJOIN Image_Headings ih ON toString(i.image_id) = toString(ih.image_id)\nJOIN RelevantHeadings rh ON toString(ih.heading_id) = toString(rh.heading_id)\nORDER BY rh.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn the realm of vector operations, the `MATCH` operator is used for performing approximate nearest neighbor (ANN) searches, which are efficient for finding items similar to a given concept. The `lembed` function generates a vector representation of the text \"Historical events,\" which allows the system to capture semantic meanings. The `k = 5` clause indicates the query is interested in the top 5 closest matches based on Euclidean distance, with smaller distances indicating higher similarity. The approach leverages embeddings from the model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', which is fine-tuned for understanding visual and textual content.\nWhat is the description of the image that paints the clearest picture of historical events?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "SELECT article_id, title FROM Articles;", + "sql_result_column_count": 2, + "sql_result_rows_count": 100, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Could you pull up all the articles for me? I'm curious to see their IDs and titles. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "SELECT article_id, title FROM Articles;" + ], + "integration_level": 0, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Could you pull up all the articles for me? I'm curious to see their IDs and titles. Thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter appoints Edmund Muskie as Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey! Can you show me the IDs of the top 3 images that have captions related to President Carter appointing Edmund Muskie as Secretary of State?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'President Carter appoints Edmund Muskie as Secretary of State') AS ref_vec_0\n\nSELECT image_id, distance(Images.caption_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you show me the IDs of the top 3 images that have captions related to President Carter appointing Edmund Muskie as Secretary of State?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Photograph of a historical figure from the early 20th century') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 10, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you provide the IDs and similarity scores for the 10 images that best match the description \"Photograph of a historical figure from the early 20th century\"?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Photograph of a historical figure from the early 20th century') AS ref_vec_0\n\nSELECT image_id, distance(Images.description_embedding, ref_vec_0) AS distance\nFROM Images\nORDER BY distance\nLIMIT 10;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you provide the IDs and similarity scores for the 10 images that best match the description \"Photograph of a historical figure from the early 20th century\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and environmental impact') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE wiki_id = 1\n)\n\nSELECT p.paragraph_id, f.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN FilteredArticles f ON toString(p.article_id) = toString(f.article_id)\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Formal", + "question": "Identify the titles and paragraph IDs of the top three paragraphs related to solar energy and environmental impact, specifically from articles belonging to wiki ID 1.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Solar energy and environmental impact') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title\n FROM Articles\n WHERE wiki_id = 1\n)\n\nSELECT p.paragraph_id, f.title, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN FilteredArticles f ON toString(p.article_id) = toString(f.article_id)\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the titles and paragraph IDs of the top three paragraphs related to solar energy and environmental impact, specifically from articles belonging to wiki ID 1.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth in literature') AS ref_vec_0,\n\nMatchedParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.url\nFROM Articles a\nJOIN MatchedParagraphs mp ON toString(a.article_id) = toString(mp.article_id);", + "sql_result_column_count": 1, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "Can you uncover the web addresses of the top three articles that delve into the journey of understanding racial injustice and the path of moral evolution through the lens of literature?", + "external_knowledge": "The `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search, which is a common technique for finding data points that are most similar to a given vector representation. The `k=3` specifies that the query should return the top 3 most similar paragraphs to the given concept. The similarity is determined based on the Euclidean distance (L2 norm), where smaller distances indicate higher similarity. The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is used to generate the embeddings, which are then used to encapsulate the concept of racial injustice and moral growth in literature.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of racial injustice and moral growth in literature') AS ref_vec_0,\n\nMatchedParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.url\nFROM Articles a\nJOIN MatchedParagraphs mp ON toString(a.article_id) = toString(mp.article_id);" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe `MATCH` operator in SQLite performs an approximate nearest neighbor (ANN) search, which is a common technique for finding data points that are most similar to a given vector representation. The `k=3` specifies that the query should return the top 3 most similar paragraphs to the given concept. The similarity is determined based on the Euclidean distance (L2 norm), where smaller distances indicate higher similarity. The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` is used to generate the embeddings, which are then used to encapsulate the concept of racial injustice and moral growth in literature.\nCan you uncover the web addresses of the top three articles that delve into the journey of understanding racial injustice and the path of moral evolution through the lens of literature?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The mechanism of Saxbe fix in the United States Constitution') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Imperative", + "question": "Could you please identify the top 5 paragraphs that closely relate to the mechanism of Saxbe fix in the United States Constitution? I need their paragraph IDs, the article IDs they belong to, and their positions within those articles.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The mechanism of Saxbe fix in the United States Constitution') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you please identify the top 5 paragraphs that closely relate to the mechanism of Saxbe fix in the United States Constitution? I need their paragraph IDs, the article IDs they belong to, and their positions within those articles.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the United States Congress') AS ref_vec_0\n\nSELECT \n p.paragraph_id AS paragraph_id, \n a.title AS title, \n a.url AS url, \n distance(p.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs p\nJOIN \n Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Imperative", + "question": "Could you please identify the top 5 paragraphs that are highly related to the concept of \"The Saxbe fix\" and its impact on the United States Congress? Also, get me the article titles and URLs for these paragraphs, and let me know their similarity distances!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a mechanism related to the United States Congress') AS ref_vec_0\n\nSELECT \n p.paragraph_id AS paragraph_id, \n a.title AS title, \n a.url AS url, \n distance(p.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs p\nJOIN \n Articles a ON toString(p.article_id) = toString(a.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you please identify the top 5 paragraphs that are highly related to the concept of \"The Saxbe fix\" and its impact on the United States Congress? Also, get me the article titles and URLs for these paragraphs, and let me know their similarity distances!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has become a relevant solution for appointments to the United States Cabinet.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Simple", + "question_style": "Colloquial", + "question": "Hey! Can you help me find the top 3 paragraphs that talk about how the Saxbe fix is a relevant solution for Cabinet appointments in the US? I'd love to know their IDs and the articles they're from!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix has become a relevant solution for appointments to the United States Cabinet.') AS ref_vec_0\n\nSELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \nFROM Paragraphs\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey! Can you help me find the top 3 paragraphs that talk about how the Saxbe fix is a relevant solution for Cabinet appointments in the US? I'd love to know their IDs and the articles they're from!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "What can you tell me about the three paragraphs that are closely related to the themes of Harper Lee's \"To Kill a Mockingbird,\" especially focusing on racial injustice and growth through Scout Finch's perspective?", + "external_knowledge": "Vector operations in this context involve using embeddings to conduct a nearest neighbor search. The \"MATCH\" operator is utilized to perform an approximate nearest neighbor (ANN) search based on the description's embedding vector. The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` generates a vector representation of the specified text, which is then compared to the vectors in the `text_embedding` column. The `k = 3` parameter ensures that only the top three paragraphs with the smallest Euclidean distances are returned, highlighting those most similar in themes of racial injustice and moral growth as depicted through the eyes of Scout Finch.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird is a timeless exploration of racial injustice and moral growth, seen through the innocent yet perceptive eyes of Scout Finch.') AS ref_vec_0\n\nSELECT paragraph_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nVector operations in this context involve using embeddings to conduct a nearest neighbor search. The \"MATCH\" operator is utilized to perform an approximate nearest neighbor (ANN) search based on the description's embedding vector. The model `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` generates a vector representation of the specified text, which is then compared to the vectors in the `text_embedding` column. The `k = 3` parameter ensures that only the top three paragraphs with the smallest Euclidean distances are returned, highlighting those most similar in themes of racial injustice and moral growth as depicted through the eyes of Scout Finch.\nWhat can you tell me about the three paragraphs that are closely related to the themes of Harper Lee's \"To Kill a Mockingbird,\" especially focusing on racial injustice and growth through Scout Finch's perspective?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms for adjusting constitutional appointments, focusing on historical processes') AS ref_vec_0,\n\nArticleSelection AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT article_id\nFROM ArticleSelection\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you provide the IDs of the top 5 articles that focus on historical processes for adjusting constitutional appointments?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanisms for adjusting constitutional appointments, focusing on historical processes') AS ref_vec_0,\n\nArticleSelection AS (\n SELECT article_id, title, url, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT article_id\nFROM ArticleSelection\nORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you provide the IDs of the top 5 articles that focus on historical processes for adjusting constitutional appointments?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional debates and historical legislative procedures') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, p.text\nFROM RelevantParagraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Constitution%'\nORDER BY p.distance\nLIMIT 5;", + "sql_result_column_count": 3, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Descriptive", + "question": "Can you give me the titles and URLs of articles related to \"Constitution\" and provide the top 5 paragraphs that discuss constitutional debates and historical legislative procedures? These paragraphs should be ranked by their relevance to the topic.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional debates and historical legislative procedures') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title, a.url, p.text\nFROM RelevantParagraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title LIKE '%Constitution%'\nORDER BY p.distance\nLIMIT 5;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCan you give me the titles and URLs of articles related to \"Constitution\" and provide the top 5 paragraphs that discuss constitutional debates and historical legislative procedures? These paragraphs should be ranked by their relevance to the topic.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional mechanism to appoint current or former Congress members') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title = 'Saxbe fix'\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 0, + "sql_complexity": "Moderate", + "question_style": "Metaphorical", + "question": "Seek the quintessence of thought: What are the five most insightful paragraphs discussing the constitutional dance of appointing current or former Congress members, drawn from the article known as \"Saxbe fix\"?", + "external_knowledge": "In this query, the `MATCH` operator is used to perform an approximate nearest neighbor search to find paragraphs whose text embeddings are similar to the given concept. The `k=5` specifies that the top 5 closest matches are returned. The model \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" is used to generate embeddings, allowing textual data to be compared in a high-dimensional vector space. The closer the paragraphs' embeddings are to the specified concept, the higher they rank in similarity, which is determined using Euclidean distance (L2 norm).", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Constitutional mechanism to appoint current or former Congress members') AS ref_vec_0\n\nSELECT p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs p\nJOIN Articles a ON toString(p.article_id) = toString(a.article_id)\nWHERE a.title = 'Saxbe fix'\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIn this query, the `MATCH` operator is used to perform an approximate nearest neighbor search to find paragraphs whose text embeddings are similar to the given concept. The `k=5` specifies that the top 5 closest matches are returned. The model \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\" is used to generate embeddings, allowing textual data to be compared in a high-dimensional vector space. The closer the paragraphs' embeddings are to the specified concept, the higher they rank in similarity, which is determined using Euclidean distance (L2 norm).\nSeek the quintessence of thought: What are the five most insightful paragraphs discussing the constitutional dance of appointing current or former Congress members, drawn from the article known as \"Saxbe fix\"?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on legal precedents in the United States') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title, p.text\nFROM FilteredArticles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index = 0\nORDER BY a.distance\nLIMIT 3;", + "sql_result_column_count": 2, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Concise", + "question": "Top 3 articles discussing legal precedents in the United States, return their titles and first paragraphs.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Discussion on legal precedents in the United States') AS ref_vec_0,\n\nFilteredArticles AS (\n SELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 3\n)\n\nSELECT a.title, p.text\nFROM FilteredArticles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nWHERE p.paragraph_index = 0\nORDER BY a.distance\nLIMIT 3;" + ], + "integration_level": 3, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nTop 3 articles discussing legal precedents in the United States, return their titles and first paragraphs.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative use of renewable energy and green design') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title, \n a.url AS url, \n sp.text AS text\nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance AS distance \nLIMIT 3;", + "sql_result_column_count": 3, + "sql_result_rows_count": 3, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 3 articles that have paragraphs discussing innovative renewable energy and green design? I need to know the articles' titles, URLs, and those paragraph snippets. Thanks!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Innovative use of renewable energy and green design') AS ref_vec_0,\n\nSimilarParagraphs AS (\n SELECT \n paragraph_id, \n article_id, \n text, \n distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM \n Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT \n a.title AS title, \n a.url AS url, \n sp.text AS text\nFROM \n Articles a\nJOIN \n SimilarParagraphs sp ON toString(a.article_id) = toString(sp.article_id)\nORDER BY \n sp.distance AS distance \nLIMIT 3;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the top 3 articles that have paragraphs discussing innovative renewable energy and green design? I need to know the articles' titles, URLs, and those paragraph snippets. Thanks!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', '\\n \\n History of Saxbe fix\\n \\n

Saxbe fix article

\\n

This article provides details about the Saxbe fix, a mechanism used by presidents

\\n \\n ') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Descriptive", + "question": "Please provide the ID and title of the article that best matches a description of the Saxbe fix, as described in the provided HTML content.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', '\\n \\n History of Saxbe fix\\n \\n

Saxbe fix article

\\n

This article provides details about the Saxbe fix, a mechanism used by presidents

\\n \\n ') AS ref_vec_0\n\nSELECT article_id, title, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\nFROM Articles\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease provide the ID and title of the article that best matches a description of the Saxbe fix, as described in the provided HTML content.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a constitutional mechanism dealing with emoluments.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT a.title\nFROM RelevantParagraphs rp\nJOIN Articles a ON toString(rp.article_id) = toString(a.article_id)\nORDER BY rp.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Metaphorical", + "question": "In the realm of constitutional mechanisms, find the title of the article that best embodies the concept of the Saxbe fix, a solution regarding emoluments.", + "external_knowledge": "Vector operations using the `MATCH` operator perform an approximate nearest neighbor (ANN) search to find items that are most similar to a particular vector representation. The `lembed()` function is used to derive embeddings from text based on specific pre-trained models like 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'. The search process ranks items by their Euclidean distance from the target embedding, with smaller distances indicating higher similarity. The Saxbe fix relates to the legal strategy used to circumvent emoluments clauses within the U.S. Constitution.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix is a constitutional mechanism dealing with emoluments.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT a.title\nFROM RelevantParagraphs rp\nJOIN Articles a ON toString(rp.article_id) = toString(a.article_id)\nORDER BY rp.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nVector operations using the `MATCH` operator perform an approximate nearest neighbor (ANN) search to find items that are most similar to a particular vector representation. The `lembed()` function is used to derive embeddings from text based on specific pre-trained models like 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'. The search process ranks items by their Euclidean distance from the target embedding, with smaller distances indicating higher similarity. The Saxbe fix relates to the legal strategy used to circumvent emoluments clauses within the U.S. Constitution.\nIn the realm of constitutional mechanisms, find the title of the article that best embodies the concept of the Saxbe fix, a solution regarding emoluments.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Ineligibility Clause prevents members of Congress from taking civil office positions created or whose emoluments are increased during their term.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelevantParagraphs\nORDER BY distance;", + "sql_result_column_count": 1, + "sql_result_rows_count": 5, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the texts of the 5 paragraphs most relevant to the concept of the Ineligibility Clause that prevents members of Congress from taking certain civil office positions, ordered by their relevance?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Ineligibility Clause prevents members of Congress from taking civil office positions created or whose emoluments are increased during their term.') AS ref_vec_0,\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, paragraph_index, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance \n FROM Paragraphs\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT text\nFROM RelevantParagraphs\nORDER BY distance;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the texts of the 5 paragraphs most relevant to the concept of the Ineligibility Clause that prevents members of Congress from taking certain civil office positions, ordered by their relevance?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause in the Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix salary rollback mechanism') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nRelevantArticles AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n),\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance AS paragraph_distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id\nFROM RelevantArticles a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.article_distance + p.paragraph_distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Highly Complex", + "question_style": "Colloquial", + "question": "Hey there! Could you find me the top 5 articles that talk about the \"Ineligibility Clause\" and have some stuff on the \"Saxbe fix salary rollback mechanism\"? I need them ordered by how well they fit both topics!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause in the Constitution') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Saxbe fix salary rollback mechanism') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 10\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nRelevantArticles AS (\n SELECT article_id, distance AS article_distance\n FROM Articles_filtered AS Articles\n),\n\nRelevantParagraphs AS (\n SELECT paragraph_id, article_id, distance AS paragraph_distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id\nFROM RelevantArticles a\nJOIN RelevantParagraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.article_distance + p.paragraph_distance\nLIMIT 5;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Could you find me the top 5 articles that talk about the \"Ineligibility Clause\" and have some stuff on the \"Saxbe fix salary rollback mechanism\"? I need them ordered by how well they fit both topics!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in the United States constitutional law') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 1, + "sql_result_rows_count": 127, + "sql_complexity": "Moderate", + "question_style": "Vague", + "question": "Can you find a handful of sections discussing the Saxbe fix's impact on U.S. constitutional law?", + "external_knowledge": "The vector search performed by the `MATCH` operator involves comparing the vector representation of article content against a query vector generated from the text \"The Saxbe fix and its implications in the United States constitutional law\". The `lembed` function utilizes a specific model ('laion/CLIP-ViT-B-32-laion2B-s34B-b79K') to transform text into vectors, allowing for semantic similarity comparison. The parameter `k=5` indicates that the search should return the top 5 items with the highest similarity. Typically, in vector searches, lower Euclidean distances between vectors represent higher similarity.", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix and its implications in the United States constitutional law') AS ref_vec_0\n\nSELECT p.paragraph_id, distance(a.raw_wikitext_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nThe vector search performed by the `MATCH` operator involves comparing the vector representation of article content against a query vector generated from the text \"The Saxbe fix and its implications in the United States constitutional law\". The `lembed` function utilizes a specific model ('laion/CLIP-ViT-B-32-laion2B-s34B-b79K') to transform text into vectors, allowing for semantic similarity comparison. The parameter `k=5` indicates that the search should return the top 5 items with the highest similarity. Typically, in vector searches, lower Euclidean distances between vectors represent higher similarity.\nCan you find a handful of sections discussing the Saxbe fix's impact on U.S. constitutional law?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed architectural overview with environmental design elements in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into the sustainable practices used in modern architecture') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.article_id, p.paragraph_index\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.distance, p.distance\nLIMIT 10;", + "sql_result_column_count": 2, + "sql_result_rows_count": 0, + "sql_complexity": "Highly Complex", + "question_style": "Concise", + "question": "Find the IDs and paragraph indices for the top 3 articles about architectural design in Chicago and the top 5 paragraphs on sustainable practices in modern architecture.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed architectural overview with environmental design elements in Chicago') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Insights into the sustainable practices used in modern architecture') AS ref_vec_1,\n\na_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 3\n),\n\np_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.article_id, p.paragraph_index\nFROM a_filtered AS a\nJOIN p_filtered AS p ON toString(a.article_id) = toString(p.article_id)\nORDER BY a.distance, p.distance\nLIMIT 10;" + ], + "integration_level": 9, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nFind the IDs and paragraph indices for the top 3 articles about architectural design in Chicago and the top 5 paragraphs on sustainable practices in modern architecture.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix was named after Senator William Saxbe') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism reducing emoluments for cabinet appointments') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT article_id, title, url, raw_html_embedding, distance\n FROM Articles_filtered AS Articles\n),\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, paragraph_index, text_embedding, distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id, a.title, pm.paragraph_index, pm.distance AS paragraph_distance\nFROM ArticleMatches a\nJOIN ParagraphMatches pm ON toString(a.article_id) = toString(pm.article_id)\nORDER BY pm.distance\nLIMIT 10;", + "sql_result_column_count": 4, + "sql_result_rows_count": 0, + "sql_complexity": "Complex", + "question_style": "Imperative", + "question": "Please find the top 10 articles and their titles, along with the corresponding paragraph index and similarity distance, where the articles are related to Senator William Saxbe and the paragraphs discuss reducing emoluments for cabinet appointments. Make sure to order them by paragraph similarity distance.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'The Saxbe fix was named after Senator William Saxbe') AS ref_vec_0,\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Mechanism reducing emoluments for cabinet appointments') AS ref_vec_1,\n\nArticles_filtered AS (\n SELECT\n *,\n distance(raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n\n ORDER BY distance\n LIMIT 5\n),\n\nParagraphs_filtered AS (\n SELECT\n *,\n distance(text_embedding, ref_vec_1) AS distance\n FROM Paragraphs\n\n ORDER BY distance\n LIMIT 10\n),\n\nArticleMatches AS (\n SELECT article_id, title, url, raw_html_embedding, distance\n FROM Articles_filtered AS Articles\n),\n\nParagraphMatches AS (\n SELECT paragraph_id, article_id, paragraph_index, text_embedding, distance\n FROM Paragraphs_filtered AS Paragraphs\n)\n\nSELECT a.article_id, a.title, pm.paragraph_index, pm.distance AS paragraph_distance\nFROM ArticleMatches a\nJOIN ParagraphMatches pm ON toString(a.article_id) = toString(pm.article_id)\nORDER BY pm.distance\nLIMIT 10;" + ], + "integration_level": 7, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nPlease find the top 10 articles and their titles, along with the corresponding paragraph index and similarity distance, where the articles are related to Senator William Saxbe and the paragraphs discuss reducing emoluments for cabinet appointments. Make sure to order them by paragraph similarity distance.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed discussion on United States constitutional law focusing on legislative processes.') AS ref_vec_0,\n\nEmbeddingSearch AS (\n SELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT article_id\nFROM EmbeddingSearch;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you fetch me the article ID for the top piece that dives deep into U.S. constitutional law and legislative processes? Just need the one that's the best fit!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'A detailed discussion on United States constitutional law focusing on legislative processes.') AS ref_vec_0,\n\nEmbeddingSearch AS (\n SELECT article_id, distance(Articles.raw_html_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT article_id\nFROM EmbeddingSearch;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you fetch me the article ID for the top piece that dives deep into U.S. constitutional law and legislative processes? Just need the one that's the best fit!\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Understanding the complexities of quantum mechanics and its implications') AS ref_vec_0,\n\nParagraphSimilarities AS (\n SELECT p.paragraph_id, p.article_id, distance(p.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN ParagraphSimilarities ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Interrogative", + "question": "Could you show me the article title that is most related to the understanding of the complexities of quantum mechanics and its implications?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Understanding the complexities of quantum mechanics and its implications') AS ref_vec_0,\n\nParagraphSimilarities AS (\n SELECT p.paragraph_id, p.article_id, distance(p.text_embedding, ref_vec_0) AS distance\n FROM Paragraphs p\n ORDER BY distance\n LIMIT 5\n)\n\nSELECT a.title\nFROM Articles a\nJOIN ParagraphSimilarities ps ON toString(a.article_id) = toString(ps.article_id)\nORDER BY ps.distance\nLIMIT 1;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the article title that is most related to the understanding of the complexities of quantum mechanics and its implications?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technological advancements in AI') AS ref_vec_0\n\nSELECT a.title, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 2, + "sql_result_rows_count": 5, + "sql_complexity": "Moderate", + "question_style": "Concise", + "question": "What are the titles and texts of the top 5 paragraphs related to technological advancements in AI?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Technological advancements in AI') AS ref_vec_0\n\nSELECT a.title, p.text, distance(p.text_embedding, ref_vec_0) AS distance\nFROM Articles a\nJOIN Paragraphs p ON toString(a.article_id) = toString(p.article_id)\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 5, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nWhat are the titles and texts of the top 5 paragraphs related to technological advancements in AI?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of renewable energy sources and their impact') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;", + "sql_result_column_count": 4, + "sql_result_rows_count": 5, + "sql_complexity": "Simple", + "question_style": "Interrogative", + "question": "Could you show me the top 5 paragraphs that discuss the exploration of renewable energy sources and their impact, including their IDs, article IDs, and positions within the articles?", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Exploration of renewable energy sources and their impact') AS ref_vec_0\n\nSELECT \n paragraph_id, \n article_id, \n paragraph_index, \n text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM \n Paragraphs\nORDER BY distance\nLIMIT 5;" + ], + "integration_level": 2, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nCould you show me the top 5 paragraphs that discuss the exploration of renewable energy sources and their impact, including their IDs, article IDs, and positions within the articles?\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;", + "sql_result_column_count": 2, + "sql_result_rows_count": 1, + "sql_complexity": "Simple", + "question_style": "Formal", + "question": "Identify the paragraph ID and text of the paragraph that best represents \"Harper Lee's To Kill a Mockingbird\" from the Paragraphs table.", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Harper Lee’s To Kill a Mockingbird') AS ref_vec_0\n\nSELECT paragraph_id, text, distance(Paragraphs.text_embedding, ref_vec_0) AS distance\nFROM Paragraphs\nORDER BY distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nIdentify the paragraph ID and text of the paragraph that best represents \"Harper Lee's To Kill a Mockingbird\" from the Paragraphs table.\n\nLet's think step by step!\n" + }, + { + "db_id": "wikipedia_multimodal", + "sql": "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause') AS ref_vec_0,\n\nArticleMatches AS (\n SELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT p.text\nFROM Paragraphs p\nJOIN ArticleMatches am ON toString(p.article_id) = toString(am.article_id)\nORDER BY am.distance\nLIMIT 1;", + "sql_result_column_count": 1, + "sql_result_rows_count": 1, + "sql_complexity": "Complex", + "question_style": "Colloquial", + "question": "Hey there! Can you grab me the paragraph from the article that's closest to the topic \"Ineligibility Clause\"? I'm looking for the top matching article's text!", + "external_knowledge": "", + "sql_candidate": [ + "WITH\n lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', 'Ineligibility Clause') AS ref_vec_0,\n\nArticleMatches AS (\n SELECT article_id, distance(Articles.raw_wikitext_embedding, ref_vec_0) AS distance\n FROM Articles\n ORDER BY distance\n LIMIT 1\n)\n\nSELECT p.text\nFROM Paragraphs p\nJOIN ArticleMatches am ON toString(p.article_id) = toString(am.article_id)\nORDER BY am.distance\nLIMIT 1;" + ], + "integration_level": 1, + "execution_status": "success", + "db_type": "myscale", + "schema": "CREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);", + "embedding_model_name": "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", + "database_note_prompt": "There are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n", + "input": "You are a senior SQL engineer. Your task is to generate a single, correct, and executable SQL query to answer the user's question based on the provided database context.\n\n## INSTRUCTIONS\n1. **Backend Adherence**: The query MUST be written for the `myscale` database backend. This is a strict requirement.\n2. **Follow Special Notes**: You MUST strictly follow all syntax, functions, or constraints described in the [Database Backend Notes]. Pay extremely close attention to this section, as it contains critical, non-standard rules.\n3. **Schema Integrity**: The query MUST ONLY use the tables and columns provided in the [Database Schema]. Do not invent or guess table or column names.\n4. **Answer the Question**: The query must directly and accurately answer the [Natural Language Question].\n5. **Output Format**: Enclose the final SQL query in a single Markdown code block formatted for SQL (` ```sql ... ``` `).\n6. **Embedding Match**: If the [EMBEDDING_MODEL_NAME] parameter is a valid string (e.g., 'all-MiniLM-L6-v2'), you MUST generate a query that includes the WHERE [EMBEDDING_COLUMN_NAME] MATCH lembed(...) clause for vector search. Otherwise, if embedding model name below the [EMBEDDING MODEL NAME] is None, , you MUST generate a standard SQL query that OMITS the entire MATCH lembed(...) clause. The query should not perform any vector search.\n7. **Embedding Name**: If a value is provided for the parameter `[EMBEDDING_MODEL_NAME]`, your generated query must contain a `lembed` function call. The first parameter to the `lembed` function MUST be the exact value of `[EMBEDDING_MODEL_NAME]`, formatted as a string literal (enclosed in single quotes). For example, if `[EMBEDDING_MODEL_NAME]` is `laion/CLIP-ViT-B-32-laion2B-s34B-b79K`, the generated SQL must include `MATCH lembed('laion/CLIP-ViT-B-32-laion2B-s34B-b79K', ...)`.\n\n## DATABASE CONTEXT\n\n[DATABASE BACKEND]:\nmyscale\n\n[DATABASE SCHEMA]:\nCREATE TABLE Articles (\n `article_id` Nullable(Int64),\n `wiki_id` Nullable(Int64),\n `title` Nullable(String),\n `url` Nullable(String),\n `raw_html` Nullable(String),\n `raw_wikitext` Nullable(String),\n `raw_html_embedding` Array(Float32),\n `raw_wikitext_embedding` Array(Float32)\n);\nCREATE TABLE Headings (\n `heading_id` Nullable(Int64),\n `heading_text` Nullable(String),\n `parent_heading_id` Nullable(Int64),\n `heading_text_embedding` Array(Float32)\n);\nCREATE TABLE Image_Headings (\n `image_id` Int64,\n `heading_id` Int64\n);\nCREATE TABLE Images (\n `image_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `filename` Nullable(String),\n `image_title` Nullable(String),\n `parsed_title` Nullable(String),\n `url` Nullable(String),\n `is_icon` Nullable(String),\n `on_commons` Nullable(String),\n `description` Nullable(String),\n `caption` Nullable(String),\n `description_embedding` Array(Float32),\n `caption_embedding` Array(Float32)\n);\nCREATE TABLE Paragraphs (\n `paragraph_id` Nullable(Int64),\n `article_id` Nullable(Int64),\n `paragraph_index` Nullable(Int64),\n `text` Nullable(String),\n `text_embedding` Array(Float32)\n);\n\n[DATABASE BACKEND NOTES]:\nThere are a few requirements you should comply with in addition:\n1. When generating SQL queries, you should prioritize utilizing K-Nearest Neighbor (KNN) searches whenever contextually appropriate. However, you must avoid unnecessary/forced KNN implementations for:\n-- Traditional relational data queries (especially for columns like: id, age, price).\n-- Cases where standard SQL operators (equality, range, or aggregation functions) are more efficient and semantically appropriate.\n2. Only columns with a vector type (like: Array(Float32) or FixedString) support KNN queries. The names of these vector columns often end with \"_embedding\". You can perform KNN searches when the column name you need to query ends with \"_embedding\" or is otherwise identified as a vector column.\n3. In MyScale, vector similarity search is performed using the `distance()` function. You must explicitly calculate the distance in the SELECT clause and give it an alias, typically \"AS distance\". This distance alias will not be implicitly generated.\n4. **MyScale Specific Syntax:** When providing a query vector (the \"needle\") for an `Array(Float32)` column, at least one number in the array *must* contain a decimal point (e.g., `[3.0, 9, 45]`). This prevents the database from misinterpreting the vector as `Array(UInt64)`, which would cause an error.\n5. The `lembed` function is used to transform a string into a semantic vector. This function should be used within a WITH clause to define the reference vector. The lembed function has two parameters: the first is the name of the embedding model used (default value: 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K'), and the second is the string content to embed. The resulting vector should be given an alias in the WITH clause.\n6. You must generate plausible and semantically relevant words or sentences for the second parameter of the `lembed` function based on the column's name, type, and comment. For example, if a column is named `product_description_embedding` and its comment is \"Embedding of the product's features and marketing text\", you could generate text like \"durable and waterproof outdoor adventure camera\".\n7. Every KNN search query MUST conclude with \"ORDER BY distance LIMIT N\" to retrieve the top-N most similar results. The LIMIT clause is mandatory for performing a KNN search and ensuring predictable performance.\n8. When combining a vector search with JOIN operations, the standard `WHERE` clause should be used to apply filters from any of the joined tables. The `ORDER BY distance LIMIT N` clause is applied after all filtering and joins are resolved.\n9. A SELECT statement should typically be ordered by a single distance calculation to perform one primary KNN search. However, subqueries can perform their own independent KNN searches, each with its own WITH clause, distance calculation, and `ORDER BY distance LIMIT N` clause.\n\n## Example of a MyScale KNN Query\nDB Schema: Some table on `articles` with a column `abstract_embedding` `Array(Float32)`.\nQuery Task: Identify the article ID of the single most relevant article discussing innovative algorithms in graph theory.\nGenerated SQL:\n```\n WITH\n lembed('all-MiniLM-L6-v2', 'innovative algorithms in graph theory.') AS ref_vec_0 \n SELECT id, distance(articles.abstract_embedding, ref_vec_0) AS distance\n FROM articles\n ORDER BY distance\n LIMIT 1;\n```\n\n\n[EMBEDDING MODEL NAME]:\nlaion/CLIP-ViT-B-32-laion2B-s34B-b79K\n\n## NATURAL LANGUAGE QUESTION\nHey there! Can you grab me the paragraph from the article that's closest to the topic \"Ineligibility Clause\"? I'm looking for the top matching article's text!\n\nLet's think step by step!\n" + } +] \ No newline at end of file diff --git a/benchmark/evaluation/README.md b/benchmark/evaluation/README.md new file mode 100644 index 0000000..728f5d8 --- /dev/null +++ b/benchmark/evaluation/README.md @@ -0,0 +1,150 @@ +# VectorSQL Evaluation Framework + +## Overview + +The VectorSQL Evaluation Framework is a comprehensive toolset for evaluating the performance of VectorSQL queries generated from natural language questions. It provides a wide range of metrics to assess the accuracy, recall, and overall quality of SQL generation, particularly for vector databases. + +## Features + +### 1. Multi-metric Evaluation +- **Exact Match**: Checks if predicted SQL exactly matches any ground truth SQL +- **Set Metrics**: Calculates precision, recall, and F1 score based on result sets +- **Ranking Metrics**: Evaluates ranking quality with MAP, MRR, and NDCG +- **LLM-based Evaluation**: Uses large language models to assess SQL semantic correctness + +### 2. Flexible Configuration +- Supports custom SQL execution functions +- Configurable LLM evaluation parameters +- Supports multiple database schemas +- Easy to integrate with existing systems + +### 3. Robust Error Handling +- Handles empty result sets gracefully +- Provides detailed error messages +- Supports various edge cases + +## Evaluation Process + +The evaluation framework follows a structured process to assess VectorSQL queries: + +1. **SQL Execution**: Executes both standard (ground truth) SQL and predicted SQL +2. **Result Collection**: Collects results from both executions +3. **Metric Calculation**: Computes various evaluation metrics +4. **LLM Evaluation**: (Optional) Performs semantic evaluation using LLM +5. **Result Compilation**: Returns comprehensive evaluation results + +## Key Functions + +### `evaluate_with_metrics` +Main evaluation function that orchestrates the entire evaluation process. + +```python +def evaluate_with_metrics( + run_sql_func, # Function to execute SQL + nl_question: str, # Natural language question + standard_sql: str, # Standard SQL + predicted_sql: str, # Predicted SQL + db_schema: str = '', # Database schema + enable_llm: bool = False # Whether to enable LLM evaluation +) -> Dict[str, Any]: + # Evaluation logic +``` + +### Metric Calculation Functions + +#### Exact Match Metrics +- `calculate_exact_match_any_gt_with_columns`: Checks exact match against any ground truth + +#### Set Metrics +- `calculate_set_metrics_with_columns`: Calculates precision, recall, and F1 score + +#### Ranking Metrics +- `calculate_ranking_metrics_with_columns`: Computes MAP, MRR, and NDCG + +### LLM-based Evaluation + +- `evaluate_vectorsql_with_llm`: Evaluates VectorSQL queries using LLM +- `calculate_llm_based_scores`: Extracts scores from LLM evaluation results + +## API Configuration for LLM Evaluation + +To enable LLM evaluation, configure the following environment variables in the `.env` file: + +``` +# LLM API Configuration +LLM_API_URL=your-llm-api-url +LLM_API_KEY=your-llm-api-key +LLM_MODEL=your-llm-model +LLM_EVALUATION_ENABLED=True +``` + +## Usage Example + +```python +from evaluation.metrics import evaluate_with_metrics + +# Define SQL execution function +def run_sql(sql): + # Implementation to execute SQL and return results + pass + +# Evaluation parameters +nl_question = "Find the most similar products to 'smartphone'" +standard_sql = "SELECT * FROM products ORDER BY distance(description_embedding, lembed('model', 'smartphone')) LIMIT 5" +predicted_sql = "SELECT * FROM products WHERE description LIKE '%smartphone%' LIMIT 5" +db_schema = "products (id INT, name VARCHAR, description VARCHAR, description_embedding Array(Float32))" + +# Run evaluation +results = evaluate_with_metrics( + run_sql, + nl_question, + standard_sql, + predicted_sql, + db_schema, + enable_llm=True +) + +# Print results +print(results) +``` + +## Evaluation Results Format + +The evaluation function returns a comprehensive dictionary with the following structure: + +```json +{ + "golden_data": [/* Standard SQL results */], + "golden_columns": [/* Standard SQL columns */], + "exact_match": 0.0, + "precision": 0.8, + "recall": 0.6, + "f1": 0.6857, + "map": 0.7, + "mrr": 1.0, + "ndcg": 0.8, + "llm_sql_skeleton_score": 1.0, + "llm_vector_component_score": 0.0, + "llm_overall_score": 0.5 +} +``` + +## Error Handling + +The framework handles various error cases: + +- **EMPTY_GOLDEN_DATA**: Standard SQL returned no results +- **EMPTY_TEST_DATA**: Predicted SQL execution failed or returned no results +- **Invalid SQL syntax**: Handled by the SQL execution function + +## Requirements + +- Python 3.8+ +- NumPy +- Requests +- PyParsing +- Dotenv + +## License + +Please refer to the project's main [LICENSE](../../LICENSE) file for license information. diff --git a/benchmark/evaluation/metrics.py b/benchmark/evaluation/metrics.py new file mode 100644 index 0000000..62bcc5a --- /dev/null +++ b/benchmark/evaluation/metrics.py @@ -0,0 +1,824 @@ +# evaluation_Framework/metrics.py + +import re +import numpy as np +from typing import Tuple, Dict, Any, Optional +import os +from dotenv import load_dotenv +import requests +import json + +# 加载环境变量 +load_dotenv() + + +def evaluate_with_metrics( + run_sql_func, # 用于执行SQL的函数，签名：(sql: str) -> Tuple[List[tuple], List[str]] + nl_question: str, + standard_sql: str, + predicted_sql: str, + db_schema: str = "", + enable_llm: bool = False, +) -> Dict[str, Any]: + """ + 使用 metrics.py 进行评估 + + Args: + run_sql_func: 用于执行SQL的函数，返回 (查询结果数据, 查询结果列名) + nl_question: 自然语言问题 + standard_sql: 标准 SQL + predicted_sql: 预测 SQL + db_schema: 数据库 schema + enable_llm: 是否启用 LLM 评估 + + Returns: + 评估结果 + """ + golden_data, golden_columns = run_sql_func(standard_sql) + test_data, test_columns = run_sql_func(predicted_sql) + + if not golden_data or (len(golden_data) == 1 and golden_data[0] == ()): + return { + "error": "Standard SQL returned no results", + "error_type": "EMPTY_GOLDEN_DATA", + "golden_data": [], + "golden_columns": [], + } + + if not test_data or (len(test_data) == 1 and test_data[0] == ()): + return { + "error": "Predicted SQL execution failed or returned no results", + "error_type": "EMPTY_TEST_DATA", + "golden_data": golden_data, + "golden_columns": golden_columns, + } + + eval_results = {"golden_data": golden_data, "golden_columns": golden_columns} + + from evaluation.metrics import calculate_exact_match_any_gt_with_columns + + exact_match = calculate_exact_match_any_gt_with_columns( + test_data, + test_columns, + [{"execution": {"status": "success", "data": golden_data, "columns": golden_columns}}], + ) + eval_results["exact_match"] = exact_match + + from evaluation.metrics import calculate_set_metrics_with_columns + + set_metrics = calculate_set_metrics_with_columns( + test_data, test_columns, golden_data, golden_columns + ) + eval_results["precision"] = set_metrics["precision"] + eval_results["recall"] = set_metrics["recall"] + eval_results["f1"] = set_metrics["f1"] + + from evaluation.metrics import calculate_ranking_metrics_with_columns + + eval_results["map"] = calculate_ranking_metrics_with_columns( + test_data, test_columns, golden_data, golden_columns, metric_type="map" + ) + eval_results["mrr"] = calculate_ranking_metrics_with_columns( + test_data, test_columns, golden_data, golden_columns, metric_type="mrr" + ) + eval_results["ndcg"] = calculate_ranking_metrics_with_columns( + test_data, test_columns, golden_data, golden_columns, metric_type="ndcg", k=None + ) + + if enable_llm: + api_config = { + "url": os.getenv("LLM_API_URL", "https://go-cn1.gptnb.ai/v1/chat/completions"), + "api_key": os.getenv("LLM_API_KEY"), + "model": os.getenv("LLM_MODEL", "gpt-4o"), + } + from evaluation.metrics import evaluate_vectorsql_with_llm, calculate_llm_based_scores + + llm_result = evaluate_vectorsql_with_llm( + nl_question=nl_question, + db_schema=db_schema, + ground_truth_query=standard_sql, + predicted_query=predicted_sql, + api_config=api_config, + ) + if llm_result: + llm_scores = calculate_llm_based_scores(llm_result) + eval_results.update(llm_scores) + + return eval_results + + +def extract_sql_from_dify_answer(dify_answer: str) -> str: + """ + 从Dify回答中提取最后执行的SQL语句。 + 支持两种格式： + 1. Executed SQL:"xxx" + 2. <｜DSML｜function_calls>...<｜DSML｜parameter name="query" string="true">SQL... + + Args: + dify_answer: Dify返回的完整回答 + + Returns: + 提取出的SQL语句，如果未找到则返回空字符串 + """ + if not dify_answer: + return "" + + if not dify_answer: + return "" + + if "Executed SQL:" in dify_answer: + sql_marker = "Executed SQL:" + marker_pos = dify_answer.find(sql_marker) + + if marker_pos == -1: + return "" + + after_marker = dify_answer[marker_pos + len(sql_marker) :].strip() + + if after_marker.startswith('"'): + sql_match = re.search(r'"([^"]*)"', after_marker) + if sql_match: + return sql_match.group(1).strip() + return after_marker[1:].strip().split("\n")[0].rstrip('"') + + sql_code_match = re.search(r"```sql\s*(.*?)\s*```", after_marker, re.DOTALL | re.IGNORECASE) + if sql_code_match: + return sql_code_match.group(1).strip() + + first_line = after_marker.split("\n")[0].strip() + if first_line: + return first_line + + return "" + + if "<｜DSML｜function_calls>" in dify_answer: + sql_match = re.search( + r'<｜DSML｜parameter name="query" string="true">\s*(.*?)\s*', + dify_answer, + re.DOTALL, + ) + if sql_match: + return sql_match.group(1).strip() + + return "" + + +def _to_comparable_set(results: list[tuple] | list[list]) -> set: + """ + 将数据库行列表转换为可比较的集合。 + 支持 list[tuple] 和 list[list] 两种格式。 + + Args: + results: 可能是 list[tuple] 或 list[list] 的数据 + + Returns: + set: 转换后的集合（元素为 tuple） + """ + if not results: + return set() + + # 检查第一个元素的类型 + if results and isinstance(results[0], list): + # 如果是 list[list]，转换为 tuple 后再创建集合 + return set(tuple(row) for row in results) + + # 已经是 list[tuple]，直接创建集合 + return set(results) + + +def _get_gt_column_count(individual_gt_results: list[list[tuple]]) -> int: + """ + Get the standard column count from ground truth results. + Assumes all ground truth results have consistent column counts. + + Args: + individual_gt_results: List of lists, each containing tuples from one GT execution + + Returns: + int: The column count from ground truth (0 if no valid GT found) + """ + if not individual_gt_results: + return 0 + + # Find the first non-empty GT result to get column count + for gt_results in individual_gt_results: + if gt_results and len(gt_results) > 0: + return len(gt_results[0]) + + return 0 + + +def _extract_values_by_column_name( + test_results: list[tuple], + test_columns: list[str], + gt_results: list[tuple], + gt_columns: list[str], +) -> Tuple[set, set]: + """ + 提取单个值进行比对。 + + 将所有非distance列的值展平成单个值的集合进行比较。 + + Args: + test_results: 测试SQL的执行结果数据 + test_columns: 测试SQL的列名列表 + gt_results: Ground truth的执行结果数据 + gt_columns: Ground truth的列名列表 + + Returns: + (test_values, golden_values) - 两个集合，包含所有非distance列的值 + """ + + if not test_results or not gt_results or not test_columns or not gt_columns: + return set(), set() + + test_values = set() + for row in test_results: + for idx, val in enumerate(row): + if idx < len(test_columns) and not _is_distance_column(test_columns[idx]): + # 只添加可哈希的值到集合中 + if isinstance(val, (list, dict, set)): + continue + test_values.add(val) + + golden_values = set() + for row in gt_results: + for idx, val in enumerate(row): + if idx < len(gt_columns) and not _is_distance_column(gt_columns[idx]): + # 只添加可哈希的值到集合中 + if isinstance(val, (list, dict, set)): + continue + golden_values.add(val) + + return test_values, golden_values + + +def _get_base_column_name(col_name: str) -> str: + """去掉表名前缀，只保留列名""" + if "." in col_name: + return col_name.split(".", 1)[1] + return col_name + + +def _is_distance_column(col_name: str) -> bool: + col_lower = col_name.lower() + return ( + "distance" in col_lower + or "vecdist" in col_lower + or "similarity" in col_lower + or "vector" in col_lower + or "embedding" in col_lower + ) + + +def _extract_columns_by_name( + test_results: list[tuple], + test_columns: list[str], + gt_results: list[tuple], + gt_columns: list[str], +) -> list[tuple]: + """ + 从测试结果中提取与ground truth列名匹配的列，并重新排序以匹配GT的列顺序。 + 提取后对结果进行去重。过滤掉distance相关的列，只比较语义内容。 + + Args: + test_results: 测试SQL的执行结果数据 + test_columns: 测试SQL的列名列表 + gt_results: Ground truth的执行结果数据 + gt_columns: Ground truth的列名列表 + + Returns: + 去重后只包含匹配列的测试结果 + """ + if not test_results or not gt_results or not test_columns or not gt_columns: + return test_results + + test_base_columns = [_get_base_column_name(col) for col in test_columns] + gt_base_columns = [_get_base_column_name(col) for col in gt_columns] + matched_indices = [] + unmatched_gt_cols = [] + + for gt_col in gt_base_columns: + if _is_distance_column(gt_col): + continue + try: + test_col_idx = test_base_columns.index(gt_col) + if not _is_distance_column(test_columns[test_col_idx]): + matched_indices.append(test_col_idx) + except ValueError: + unmatched_gt_cols.append(gt_col) + + # 如果有 GT 列没有匹配到，返回不匹配 + if unmatched_gt_cols: + print(f" [DEBUG] 有 {len(unmatched_gt_cols)} 个 GT 列未匹配，返回空结果") + return [] + + aligned_results = [] + for test_row in test_results: + if len(test_row) > max(matched_indices): + aligned_row = tuple(test_row[i] for i in matched_indices) + aligned_results.append(aligned_row) + + deduplicated_results = [] + seen_rows = set() + for row in aligned_results: + if row not in seen_rows: + deduplicated_results.append(row) + seen_rows.add(row) + + return deduplicated_results + + +def _extract_columns_by_name_without_dedup( + test_results: list[tuple], + test_columns: list[str], + gt_results: list[tuple], + gt_columns: list[str], +) -> list[tuple]: + """ + 从测试结果中提取与ground truth列名匹配的列，并重新排序以匹配GT的列顺序。 + 提取后不去重。 + + Args: + test_results: 测试SQL的执行结果数据 + test_columns: 测试SQL的列名列表 + gt_results: Ground truth的执行结果数据 + gt_columns: Ground truth的列名列表 + + Returns: + 不去重的匹配列的测试结果 + """ + if not test_results or not gt_results or not test_columns or not gt_columns: + return test_results + + test_base_columns = [_get_base_column_name(col) for col in test_columns] + # 找到测试结果中与GT列名匹配的列索引 + matched_indices = [] + for gt_col in gt_columns: + try: + test_col_idx = test_base_columns.index(gt_col) + matched_indices.append(test_col_idx) + except ValueError: + # GT中的列在测试结果中不存在，跳过这个列 + continue + + if not matched_indices: + # 没有匹配的列，返回空结果 + return [] + + # 提取匹配的列数据 + aligned_results = [] + for test_row in test_results: + if len(test_row) > max(matched_indices): # 确保行数据足够长 + aligned_row = tuple(test_row[i] for i in matched_indices) + aligned_results.append(aligned_row) + + return aligned_results + + +def _get_gt_columns(individual_gt_results: list[dict]) -> list[str]: + """ + 获取ground truth的标准列名列表。 + 假定所有ground truth都有一致的列结构，使用第一个成功的GT作为标准。 + + Args: + individual_gt_results: 包含执行结果的字典列表，每个字典包含'execution'字段 + + Returns: + 列名列表，如果没有找到有效的GT则返回空列表 + """ + for gt_result in individual_gt_results: + gt_execution = gt_result.get("execution", {}) + if gt_execution.get("status") == "success" and gt_execution.get("columns"): + return gt_execution["columns"] + return [] + + +def calculate_exact_match_any_gt_with_columns( + test_results: list[tuple], test_columns: list[str], individual_gt_results: list[dict] +) -> float: + """ + Calculate exact match against any individual ground truth using column name matching. + Returns 1.0 if test results exactly match any single ground truth, 0.0 otherwise. + + 边界情况处理： + - 所有 GT 和 test 都为空：返回 1.0（完全匹配） + - 只有所有 GT 为空：返回 0.0 + - 只有 test 为空：检查是否有 GT 也为空，如果有则返回 1.0 + + Args: + test_results: List of tuples from test execution + test_columns: Column names from test execution + individual_gt_results: List of dicts containing GT execution results with columns + + Returns: + 1.0 if exact match with any GT, 0.0 otherwise + """ + # 边界情况：test 为空且所有 GT 也为空 + if not test_results and all( + not gt.get("execution", {}).get("data", []) for gt in individual_gt_results + ): + return 1.0 + + for gt_result in individual_gt_results: + gt_execution = gt_result.get("execution", {}) + if gt_execution.get("status") != "success": + continue + + gt_data = gt_execution.get("data", []) + gt_columns = gt_execution.get("columns", []) + gt_columns = [col for col in gt_columns if col != "distance"] + + if not gt_data: + # If this GT is empty and test is also empty, it's a match + if not test_results: + return 1.0 + continue + + # 如果 test 为空但 GT 不为空，继续检查其他 GT + if not test_results: + continue + + # Extract matching columns from test results + aligned_test_results = _extract_columns_by_name( + test_results, test_columns, gt_data, gt_columns + ) + gt_data = _extract_columns_by_name(gt_data, gt_columns, gt_data, gt_columns) + + # Compare aligned results + aligned_test_set = _to_comparable_set(aligned_test_results) + gt_set = _to_comparable_set(gt_data) + + if aligned_test_set == gt_set: + return 1.0 + + return 0.0 + + +def calculate_set_metrics_with_columns( + test_results: list[tuple], + test_columns: list[str], + golden_data: list[tuple], + golden_columns: list[str], +) -> dict: + """ + Calculate set-based metrics using single-value comparison. + + 边界情况处理： + - 两者都为空：precision=1.0, recall=1.0, F1=1.0（完全匹配） + - 只有 golden_data 为空：precision=0.0, recall=1.0, F1=0.0（假阳性） + - 只有 test_results 为空：precision=1.0, recall=0.0, F1=0.0（假阴性） + + Args: + test_results: List of tuples from test execution + test_columns: Column names from test execution + golden_data: List of tuples from golden/reference execution + golden_columns: Column names from golden execution + + Returns: + dict: Dictionary containing precision, recall, and F1 scores + """ + if not test_results and not golden_data: + return {"precision": 1.0, "recall": 1.0, "f1": 1.0} + + if not golden_data and test_results: + return {"precision": 0.0, "recall": 1.0, "f1": 0.0} + + if not test_results and golden_data: + return {"precision": 1.0, "recall": 0.0, "f1": 0.0} + + test_values, golden_values = _extract_values_by_column_name( + test_results, test_columns, golden_data, golden_columns + ) + + intersection = test_values & golden_values + print("test_values: ", test_values) + print("golden_values: ", golden_values) + print("intersection: ", intersection) + + precision = len(intersection) / len(test_values) if test_values else 0.0 + recall = len(intersection) / len(golden_values) if golden_values else 0.0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0 + + return {"precision": precision, "recall": recall, "f1": f1} + + +def calculate_ranking_metrics_with_columns( + test_results: list[tuple], + test_columns: list[str], + golden_data: list[tuple], + golden_columns: list[str], + metric_type: str, + k: int | None = None, +) -> float: + """ + Calculate ranking-based metrics using column name matching. + + 边界情况处理： + - 两者都为空：返回 1.0（完全匹配） + - 只有 golden_data 为空：返回 0.0（没有相关项） + - 只有 test_results 为空：返回 0.0（没有检索到任何项） + + Args: + test_results: List of tuples from test execution + test_columns: Column names from test execution + golden_data: List of tuples from golden execution + golden_columns: Column names from golden execution + metric_type: Type of metric ('map', 'mrr', 'ndcg') + k: Parameter for NDCG@k + + Returns: + float: Calculated metric value + """ + # 边界情况 1：两者都为空 - 完全匹配 + if not test_results and not golden_data: + return 1.0 + + # 边界情况 2：只有 golden_data 为空 + if not golden_data: + return 0.0 + + # 边界情况 3：只有 test_results 为空 + if not test_results: + return 0.0 + + # Extract matching columns from test results + aligned_test_results = _extract_columns_by_name( + test_results, test_columns, golden_data, golden_columns + ) + + # For NDCG, we need to preserve the original golden_data before deduplication + # to correctly count occurrences for graded relevance + if metric_type == "ndcg": + # 先提取列对齐（不去重），用于统计原始出现次数 + original_golden_data = _extract_columns_by_name_without_dedup( + golden_data, golden_columns, golden_data, golden_columns + ) + + # Build graded golden set from original data (count occurrences) + graded_golden_set = {} + for row in original_golden_data: + graded_golden_set[row] = graded_golden_set.get(row, 0) + 1 + + # Calculate DCG@k + dcg = 0.0 + for i, result in enumerate(aligned_test_results[:k]): + relevance = graded_golden_set.get(result, 0) + if relevance > 0: + dcg += relevance / np.log2(i + 2) + + if dcg == 0: + return 0.0 + + # Calculate IDCG@k + ideal_relevances = sorted(graded_golden_set.values(), reverse=True) + idcg = 0.0 + for i, relevance in enumerate(ideal_relevances[:k]): + idcg += relevance / np.log2(i + 2) + + return dcg / idcg if idcg > 0 else 0.0 + + # For MAP and MRR, use deduplicated golden data (existing behavior) + golden_data_dedup = _extract_columns_by_name( + golden_data, golden_columns, golden_data, golden_columns + ) + golden_set = _to_comparable_set(golden_data_dedup) + + if not aligned_test_results or not golden_set: + return 0.0 + + if metric_type == "map": + hits = 0 + sum_precisions = 0.0 + for i, result in enumerate(aligned_test_results): + if result in golden_set: + hits += 1 + precision_at_k = hits / (i + 1) + sum_precisions += precision_at_k + return sum_precisions / len(golden_set) if hits > 0 else 0.0 + + elif metric_type == "mrr": + for i, result in enumerate(aligned_test_results): + if result in golden_set: + return 1.0 / (i + 1) + return 0.0 + + return 0.0 + + +# ============================================================================== +# LLM-based VectorSQL Query evaluation +# ============================================================================== + + +def extract_and_parse_json(model_output_text: str) -> Dict[str, Any]: + """ + 从可能包含无关文本或 Markdown 标记的字符串中提取并解析 JSON 对象。 + + Args: + model_output_text: 模型返回的原始字符串内容 + + Returns: + 解析后的 Python 字典 + + Raises: + ValueError: 如果在文本中找不到有效的 JSON 对象 + json.JSONDecodeError: 如果找到的字符串不是有效的 JSON + """ + # 使用正则表达式查找从 '{' 开始到 '}' 结束的最大可能块 + json_match = re.search(r"\{.*\}", model_output_text, re.DOTALL) + + if not json_match: + raise ValueError("在模型的输出中未能找到有效的 JSON 对象。") + + json_string = json_match.group(0) + + try: + return json.loads(json_string) + except json.JSONDecodeError as e: + print("❌ 解析提取出的 JSON 字符串时失败。") + print("提取出的内容:", json_string) + raise e + + +def evaluate_vectorsql_with_llm( + nl_question: str, + db_schema: str, + ground_truth_query: str, + predicted_query: str, + api_config: Dict[str, str], + timeout: int = 60, +) -> Optional[Dict[str, Any]]: + """ + 使用 LLM 评估 VectorSQL 查询的正确性。 + + Args: + nl_question: 自然语言问题 + db_schema: 数据库 schema (DDL) + ground_truth_query: Ground truth SQL 查询 + predicted_query: 待评估的预测 SQL 查询 + api_config: API 配置，包含 'url', 'api_key', 'model' + timeout: API 请求超时时间（秒） + + Returns: + 包含评估结果的字典，如果评估失败则返回 None + 结构：{ + "sql_skeleton_evaluation": {...}, + "vector_component_evaluation": {...} + } + """ + + # 构建评估 prompt + prompt = """ +You are an expert SQL analyst and data scientist, specializing in evaluating the correctness of complex database queries that combine structured SQL predicates with semantic vector search. Your task is to meticulously evaluate a predicted VectorSQL query against a ground-truth query, considering the user's natural language question and the database schema. + +Your evaluation must be decomposed into two independent parts: **SQL Skeleton Accuracy** and **Vector Component Accuracy**. + +**1. SQL Skeleton Accuracy (`ACC_SQL`)**: +Evaluate the correctness of the standard, non-vector parts of the SQL query. The final score is 1 only if **ALL** structural components are logically equivalent to the ground truth, otherwise it is 0. +- **SELECT**: Are the correct columns and aggregations selected? +- **FROM/JOIN**: Are the correct tables and join conditions used? +- **WHERE**: Are all non-vector filtering conditions correct? +- **GROUP BY/HAVING**: Is the grouping and aggregation filtering logic correct? +- **ORDER BY**: Is the non-vector sorting logic correct? + +**2. Vector Component Accuracy (`ACC_Vec`)**: +Evaluate the correctness of the semantic search part of the query. The final score is 1 only if the vector search is semantically correct and will retrieve the intended results, otherwise it is 0. +- **Vector Column**: Is the correct vector column used for the search? +- **Vector Operation**: Is the correct distance/similarity function used (e.g., `<->`, `L2Distance`)? +- **Query Text**: Is the text used for embedding **semantically equivalent** to the one in the ground truth? For example, "AI research" is equivalent to "papers on artificial intelligence". This is the most critical check. +- **Top-K (LIMIT)**: Is the number of results to retrieve correct as per the user's question? + +Here is the information for your evaluation: + +**Natural Language Question:** + +{nl_question} + +**Database Schema:** + +```sql +{db_schema} +``` + +**Ground Truth VectorSQL Query:** + +```sql +{ground_truth_query} +``` + +**Predicted VectorSQL Query to Evaluate:** + +```sql +{predicted_query} +``` + +Based on your analysis, provide the evaluation in a single JSON object. Do not include any text or explanations outside of the JSON object. + +**JSON Output Format:** + +```json +{{ + "sql_skeleton_evaluation": {{ + "reasoning": "Provide a brief explanation for the SQL skeleton score.", + "select_correct": , + "from_join_correct": , + "where_correct": , + "groupby_having_correct": , + "orderby_correct": , + "score": <1_or_0> + }}, + "vector_component_evaluation": {{ + "reasoning": "Provide a brief explanation for the vector component score, focusing on the semantic similarity of the query text.", + "vector_column_correct": , + "vector_operation_correct": , + "query_text_semantically_correct": , + "top_k_correct": , + "score": <1_or_0> + }} +}} +``` +""" + + # 填充 prompt + final_prompt = prompt.format( + nl_question=nl_question, + db_schema=db_schema, + ground_truth_query=ground_truth_query, + predicted_query=predicted_query, + ) + + # 准备 API 请求 + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {api_config['api_key']}", + } + + payload = { + "model": api_config["model"], + "messages": [{"role": "user", "content": final_prompt}], + "temperature": 0.0, + "stream": False, + } + + try: + # 发送 API 请求 + response = requests.post(api_config["url"], headers=headers, json=payload, timeout=timeout) + response.raise_for_status() + api_response_data = response.json() + + # 提取响应内容 + message_content = api_response_data["choices"][0]["message"]["content"] + + # 解析 JSON 结果 + evaluation_result = extract_and_parse_json(message_content) + + return evaluation_result + + except requests.exceptions.RequestException as e: + print(f"❌ LLM API 请求错误: {e}") + return None + except (KeyError, IndexError) as e: + print(f"❌ 解析 API 响应失败: {e}") + return None + except ValueError as e: + print(f"❌ {e}") + return None + except json.JSONDecodeError as e: + print(f"❌ JSON 解析失败: {e}") + return None + + +def calculate_llm_based_scores(evaluation_result: Optional[Dict[str, Any]]) -> Dict[str, float]: + """ + 从 LLM 评估结果中提取分数。 + + Args: + evaluation_result: LLM 返回的评估结果字典 + + Returns: + 包含各项分数的字典 + """ + if not evaluation_result: + return { + "llm_sql_skeleton_score": 0.0, + "llm_vector_component_score": 0.0, + "llm_overall_score": 0.0, + } + + try: + sql_score = evaluation_result.get("sql_skeleton_evaluation", {}).get("score", 0) + vec_score = evaluation_result.get("vector_component_evaluation", {}).get("score", 0) + + return { + "llm_sql_skeleton_score": float(sql_score), + "llm_vector_component_score": float(vec_score), + "llm_overall_score": (float(sql_score) + float(vec_score)) / 2.0, + } + except (KeyError, ValueError, TypeError) as e: + print(f"❌ 提取 LLM 评分失败: {e}") + return { + "llm_sql_skeleton_score": 0.0, + "llm_vector_component_score": 0.0, + "llm_overall_score": 0.0, + } diff --git a/benchmark/figures/mcp_vector_sql.png b/benchmark/figures/mcp_vector_sql.png new file mode 100644 index 0000000..ec245c4 Binary files /dev/null and b/benchmark/figures/mcp_vector_sql.png differ diff --git a/benchmark/script/filter_clean_samples.py b/benchmark/script/filter_clean_samples.py new file mode 100644 index 0000000..d0a47a3 --- /dev/null +++ b/benchmark/script/filter_clean_samples.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +import re +import json + + +def filter_clean_samples(): + """ + 从日志文件中提取recall>0.2的样本ID， + 然后从原始数据文件中过滤出这些样本， + 生成格式与原始文件一致的clean_data.json + """ + # 定义文件路径 + log_file = "./log/CleanData/0113_lab.log" + data_file = "./data/results/test/olympics/clean_data.json" + output_file = "./script/clean_data.json" + + # 读取日志文件并提取所有样本的recall信息 + all_samples_recall = [] + + with open(log_file, "r") as f: + log_content = f.read() + + # 使用正则表达式匹配每个样本的ID和recall值 + sample_pattern = r"📝 样本 (\d+)/\d+.*?Recall:\s+([\d.]+)" + sample_records = re.findall(sample_pattern, log_content, re.DOTALL) + + # 解析所有样本的recall值 + for record in sample_records: + sample_id = int(record[0]) + recall = float(record[1]) + all_samples_recall.append({"sample_id": sample_id, "recall": recall}) + + # 筛选出recall>0.2的样本ID + good_sample_ids = set() + for sample in all_samples_recall: + if sample["recall"] > 0.2: + good_sample_ids.add(sample["sample_id"]) + + print(f"共找到 {len(good_sample_ids)} 个recall>0.2的样本") + + # 读取原始数据文件 + with open(data_file, "r") as f: + raw_data = json.load(f) + + # 过滤出recall>0.2的样本（注意：原始数据索引从0开始，样本ID从1开始） + clean_data = [] + for idx, sample in enumerate(raw_data): + sample_id = idx + 1 # 样本ID从1开始 + if sample_id in good_sample_ids: + clean_data.append(sample) + + # 将结果保存到JSON文件 + with open(output_file, "w", encoding="utf-8") as f: + json.dump(clean_data, f, ensure_ascii=False, indent=2) + + # 打印结果摘要 + print(f"已将 {len(clean_data)} 个优质样本保存到 {output_file}") + + +if __name__ == "__main__": + filter_clean_samples() diff --git a/benchmark/script/filter_duplicate.py b/benchmark/script/filter_duplicate.py new file mode 100644 index 0000000..1eee879 --- /dev/null +++ b/benchmark/script/filter_duplicate.py @@ -0,0 +1,46 @@ +import json + + +def filter_duplicate_samples(input_file_1, input_file_2, output_file): + # 读取输入文件 + with open(input_file_1, "r", encoding="utf-8") as f: + data_1 = json.load(f) + + with open(input_file_2, "r", encoding="utf-8") as f: + data_2 = json.load(f) + + print(f"输入文件1样本数量: {len(data_1)}") + print(f"输入文件2样本数量: {len(data_2)}") + + # 使用question作为唯一key进行去重 + unique_samples_dict = {} + + for sample in data_1 + data_2: + # 获取question字段作为唯一键 + question = sample.get("sql", "") + # 如果question为空，使用样本的JSON字符串作为备选键 + if not question: + question = json.dumps(sample, ensure_ascii=False, sort_keys=True) + unique_samples_dict[question] = sample + + # 提取去重后的样本 + unique_samples = list(unique_samples_dict.values()) + + print(f"去重后样本数量: {len(unique_samples)}") + print(f"重复样本数量: {len(data_1 + data_2) - len(unique_samples)}") + + # 保存结果到输出文件 + with open(output_file, "w", encoding="utf-8") as f: + json.dump(unique_samples, f, ensure_ascii=False, indent=2) + + print(f"去重结果已保存到: {output_file}") + + +if __name__ == "__main__": + # 输入文件路径 + input_file_1 = "./data/results/test/olympics/0112/success_samples.json" + input_file_2 = "./data/results/test/olympics/merged_data.json" + # 输出文件路径（避免覆盖原始文件，使用新的文件名） + output_file = "./data/results/test/olympics/0112/unique_samples.json" + + filter_duplicate_samples(input_file_1, input_file_2, output_file) diff --git a/benchmark/script/filter_poor_samples.py b/benchmark/script/filter_poor_samples.py new file mode 100644 index 0000000..069049a --- /dev/null +++ b/benchmark/script/filter_poor_samples.py @@ -0,0 +1,109 @@ +import re +import json + + +def filter_poor_recall_samples(): + # 定义文件路径 + log_file = "./log/CleanData/0120_lembed_sql.log" + data_file = "./data/results/test/olympics/olympics_qs.json" + output_file = "./data/results/test/olympics/poor_less_than_06.json" + + # 读取日志文件并提取样本信息 + poor_samples = [] + + with open(log_file, "r") as f: + log_content = f.read() + + # 使用正则表达式匹配每个完整的样本块 + sample_block_pattern = r"📝 样本 (\d+)/\d+.*?预测SQL: (.*?)\s+步骤3: 使用metrics.py进行评估.*?test_values: (.*?)\ngolden_values: (.*?)\nintersection: (.*?)\n.*?Recall:\s+([\d.]+)" + sample_blocks = re.findall(sample_block_pattern, log_content, re.DOTALL) + + # 筛选出recall<=0.2的样本并提取所有需要的字段 + for block in sample_blocks: + sample_id = int(block[0]) + predicted_sql = block[1].strip() + test_values_str = block[2] + golden_values_str = block[3] + intersection_str = block[4] + recall = float(block[5]) + + if recall < 0.6: + # 解析集合字符串为Python对象 + try: + # 处理可能的JSON格式字符串（如使用双引号的情况） + if test_values_str.startswith('{"'): + test_values = eval(test_values_str.replace('"', "'")) + else: + test_values = eval(test_values_str) + + if golden_values_str.startswith('{"'): + golden_values = eval(golden_values_str.replace('"', "'")) + else: + golden_values = eval(golden_values_str) + + if intersection_str == "set()": + intersection = set() + elif intersection_str.startswith('{"'): + intersection = eval(intersection_str.replace('"', "'")) + else: + intersection = eval(intersection_str) + except Exception: + # 如果解析失败，使用空集合 + test_values = set() + golden_values = set() + intersection = set() + + poor_samples.append( + { + "sample_id": sample_id, + "recall": recall, + "predicted_sql": predicted_sql, + "test_values": list(test_values), + "golden_values": list(golden_values), + "intersection": list(intersection), + } + ) + + print(f"共找到 {len(poor_samples)} 个recall<=0.6的样本") + + # 读取原始数据文件 + with open(data_file, "r") as f: + raw_data = json.load(f) + + # 映射样本ID到原始数据（注意：原始数据索引从0开始，样本ID从1开始） + results = [] + for sample in poor_samples: + raw_index = sample["sample_id"] - 1 + if raw_index < 0 or raw_index >= len(raw_data): + print(f"警告：样本ID {sample['sample_id']} 超出原始数据范围！") + continue + + raw_sample = raw_data[raw_index].copy() # 复制原始样本的所有数据 + # # 添加样本ID、recall值和新字段 + # raw_sample['sample_id'] = sample['sample_id'] + # raw_sample['recall'] = sample['recall'] + # raw_sample['predicted_sql'] = sample['predicted_sql'] + # raw_sample['test_values'] = sample['test_values'] + # raw_sample['golden_values'] = sample['golden_values'] + # raw_sample['intersection'] = sample['intersection'] + # # 保留原始的sql字段，添加一个standard_sql的别名 + # raw_sample['standard_sql'] = raw_sample.get('sql', '') + results.append(raw_sample) + + # 将结果保存到JSON文件 + with open(output_file, "w", encoding="utf-8") as f: + json.dump(results, f, ensure_ascii=False, indent=2) + + # 打印结果摘要 + print(f"已将 {len(results)} 个样本的详细信息保存到 {output_file}") + # print("\n样本详情：") + # for result in results: + # print(f"\n样本ID: {result['sample_id']}") + # print(f"Recall: {result['recall']}") + # print(f"问题: {result['question'][:100]}...") + # print(f"SQL复杂度: {result['sql_complexity']}") + # print(f"集成级别: {result['integration_level']}") + + +if __name__ == "__main__": + filter_poor_recall_samples() diff --git a/benchmark/script/filter_success_samples.py b/benchmark/script/filter_success_samples.py new file mode 100644 index 0000000..544128a --- /dev/null +++ b/benchmark/script/filter_success_samples.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +根据Recall值过滤样本的脚本 + +用途：从JSON格式的基准测试结果中，根据Recall值过滤样本 + +使用方法： +python filter_success_samples.py +""" + +import json +import os + + +def filter_samples_by_recall(input_file, output_file, recall_threshold=0.6, operator="<"): + """ + 根据Recall值过滤样本 + + Args: + input_file (str): 输入JSON文件路径 + output_file (str): 输出JSON文件路径 + recall_threshold (float): Recall阈值，默认0.6 + operator (str): 比较操作符，可以是"<", "<=", ">", ">=", "==", "!="，默认"<" + + Returns: + None + """ + # 参数验证 + if not os.path.exists(input_file): + print(f"错误：输入文件 {input_file} 不存在！") + return + + if operator not in ["<", "<=", ">", ">=", "==", "!="]: + print(f"错误：无效的操作符 {operator}，请使用 <, <=, >, >=, ==, !=") + return + + try: + # 读取输入文件 + with open(input_file, "r", encoding="utf-8") as f: + data = json.load(f) + + # 从'details'字段获取样本列表 + samples = data.get("details", []) + print(f"成功读取输入文件，共 {len(samples)} 个样本") + + # 过滤样本 + filtered_samples = [] + for i, sample in enumerate(samples): + # 跳过非字典类型的样本 + if not isinstance(sample, dict): + print(f"警告：样本 {i + 1} 不是字典类型，跳过处理") + continue + + # 获取evaluation字段 + evaluation = sample.get("evaluation") + if evaluation is None: + continue + + # 跳过evaluation不是字典的样本 + if not isinstance(evaluation, dict): + print(f"警告：样本 {i + 1} 的evaluation不是字典类型，跳过处理") + continue + + # 获取recall值（注意字段名是小写的'recall'） + recall = evaluation.get("recall", 0.0) + + # 确保recall是数值类型 + if not isinstance(recall, (int, float)): + print(f"警告：样本 {i + 1} 的recall值不是数值类型，跳过处理") + continue + + # 根据操作符过滤 + match operator: + case "<": + if recall < recall_threshold: + filtered_samples.append(sample) + case "<=": + if recall <= recall_threshold: + filtered_samples.append(sample) + case ">": + if recall > recall_threshold: + filtered_samples.append(sample) + case ">=": + if recall >= recall_threshold: + filtered_samples.append(sample) + case "==": + if recall == recall_threshold: + filtered_samples.append(sample) + case "!=": + if recall != recall_threshold: + filtered_samples.append(sample) + + # 保存结果到输出文件 + with open(output_file, "w", encoding="utf-8") as f: + # 先将filtered_samples中的SQL字符串处理一下，让换行符更美观 + for sample in filtered_samples: + # 处理standard_sql + if "standard_sql" in sample and isinstance(sample["standard_sql"], str): + # 确保SQL中的换行符被正确处理 + sample["standard_sql"] = sample["standard_sql"].strip() + # 处理predicted_sql + if "predicted_sql" in sample and isinstance(sample["predicted_sql"], str): + # 确保SQL中的换行符被正确处理 + sample["predicted_sql"] = sample["predicted_sql"].strip() + # 确保evaluation字段的所有信息都被保留 + if "evaluation" in sample: + # 确保evaluation是字典类型 + if isinstance(sample["evaluation"], dict): + # 保留evaluation字段的所有内容，包括golden_data和golden_columns + pass + + # 生成JSON并写入文件 + json_str = json.dumps(filtered_samples, ensure_ascii=False, indent=2) + f.write(json_str) + + # 同时生成一个SQL可读版本的文件，方便查看 + readable_output_file = output_file.replace(".json", "_readable.json") + with open(readable_output_file, "w", encoding="utf-8") as rf: + # 生成一个更易读的版本，适合直接查看 + for i, sample in enumerate(filtered_samples): + rf.write(f"\n{'=' * 80}\n") + rf.write(f"样本 {i + 1}: {sample.get('sample_id', 'N/A')}\n") + rf.write(f"{'=' * 80}\n") + rf.write(f"问题: {sample.get('question', 'N/A')}\n\n") + rf.write(f"标准SQL:\n{sample.get('standard_sql', 'N/A')}\n\n") + rf.write(f"预测SQL:\n{sample.get('predicted_sql', 'N/A')}\n\n") + + # 打印evaluation字段的详细信息，包括golden_data和golden_columns + evaluation = sample.get("evaluation", {}) + if evaluation: + rf.write("评估结果:\n") + rf.write(f" Recall: {evaluation.get('recall', 'N/A')}\n") + rf.write(f" Precision: {evaluation.get('precision', 'N/A')}\n") + rf.write(f" F1: {evaluation.get('f1', 'N/A')}\n") + rf.write(f" Exact Match: {evaluation.get('exact_match', 'N/A')}\n") + + # 打印golden_data和golden_columns + if "golden_data" in evaluation: + rf.write(f" Golden Data: {evaluation['golden_data']}\n") + if "golden_columns" in evaluation: + rf.write(f" Golden Columns: {evaluation['golden_columns']}\n") + + print(f"\n可读版本已保存到: {readable_output_file}") + + print("过滤完成！") + print(f"原始样本数量: {len(samples)}") + print(f"过滤后样本数量: {len(filtered_samples)}") + print(f"结果已保存到: {output_file}") + print(f"过滤条件: Recall {operator} {recall_threshold}") + + except json.JSONDecodeError: + print(f"错误：输入文件 {input_file} 不是有效的JSON格式！") + except Exception as e: + print(f"错误：处理文件时发生异常 - {str(e)}") + import traceback + + traceback.print_exc() + + +if __name__ == "__main__": + # 输入文件路径 + input_file = "./0120/CleanData/benchmark_results-20260120-235803.json" + # 输出文件路径 + output_file = "./tools/recall_less_than_06.json" + + # 过滤Recall < 0.6的样本 + filter_samples_by_recall(input_file, output_file, recall_threshold=0.6, operator="<") diff --git a/benchmark/script/filter_synthea_samples.py b/benchmark/script/filter_synthea_samples.py new file mode 100644 index 0000000..db85794 --- /dev/null +++ b/benchmark/script/filter_synthea_samples.py @@ -0,0 +1,38 @@ +import json +import shutil + + +def filter_synthea_samples(file_path): + # 读取原始文件 + with open(file_path, "r", encoding="utf-8") as f: + data = json.load(f) + + # 筛选出db_id为"synthea"且execution_status为"success"的样本 + synthea_samples = [ + sample + for sample in data + if sample.get("db_id") == "synthea" and sample.get("execution_status") == "success" + ] + + print(f"原始样本数量: {len(data)}") + print(f"筛选后样本数量: {len(synthea_samples)}") + print(f"保留比例: {len(synthea_samples) / len(data) * 100:.2f}%") + + # 备份原始文件 + backup_path = file_path + ".backup" + shutil.copy2(file_path, backup_path) + print(f"原始文件已备份到: {backup_path}") + + # 将筛选结果保存回原文件 + with open(file_path, "w", encoding="utf-8") as f: + json.dump(synthea_samples, f, ensure_ascii=False, indent=2) + + print(f"筛选结果已保存到: {file_path}") + + +if __name__ == "__main__": + # 文件路径 + file_path = "./data/results/test/synthea/candidate_sql.json" + + # 执行筛选 + filter_synthea_samples(file_path) diff --git a/benchmark/script/filter_zero_entries.py b/benchmark/script/filter_zero_entries.py new file mode 100644 index 0000000..0c929a9 --- /dev/null +++ b/benchmark/script/filter_zero_entries.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 + +import json +import os + + +def filter_zero_recall_samples(benchmark_file_path, data_file_path, output_file_path): + """ + 从基准测试结果文件中提取recall=0.0的样本编号，然后从原始数据文件中提取对应的完整样本 + + Args: + benchmark_file_path (str): 基准测试结果JSON文件路径 + data_file_path (str): 原始数据JSON文件路径 + output_file_path (str): 输出JSON文件路径 + """ + # 检查输入文件是否存在 + if not os.path.exists(benchmark_file_path): + print(f"错误: 基准测试结果文件不存在 - {benchmark_file_path}") + return False + + if not os.path.exists(data_file_path): + print(f"错误: 原始数据文件不存在 - {data_file_path}") + return False + + try: + # 读取基准测试结果文件 + with open(benchmark_file_path, "r", encoding="utf-8") as f: + benchmark_data = json.load(f) + + # 检查基准测试数据结构 + if not isinstance(benchmark_data, dict) or "details" not in benchmark_data: + print("错误: 基准测试数据格式不正确，缺少'details'字段") + return False + + # 提取recall=0.0的sample_id + details = benchmark_data["details"] + zero_recall_samples = [item for item in details if item.get("recall") == 0.0] + + # 统计过滤前的条目数 + total_samples = len(details) + zero_recall_count = len(zero_recall_samples) + + print(f"基准测试结果总样本数: {total_samples}") + print(f"recall=0.0的样本数: {zero_recall_count}") + + if zero_recall_count == 0: + print("警告: 没有找到recall=0.0的样本") + return False + + # 提取sample_id并转换为索引（sample_id从1开始，索引从0开始） + sample_ids = [item["sample_id"] for item in zero_recall_samples] + indices = [sample_id - 1 for sample_id in sample_ids] + + print(f"recall=0.0的sample_id: {sample_ids}") + print(f"对应的原始数据索引: {indices}") + + # 读取原始数据文件 + with open(data_file_path, "r", encoding="utf-8") as f: + data = json.load(f) + + # 检查数据是否为列表 + if not isinstance(data, list): + print("错误: 原始数据不是数组格式") + return False + + # 检查索引是否有效 + max_index = len(data) - 1 + invalid_indices = [idx for idx in indices if idx < 0 or idx > max_index] + if invalid_indices: + print(f"错误: 以下索引无效 - {invalid_indices}") + return False + + # 从原始数据中提取对应的样本 + filtered_samples = [data[idx] for idx in indices] + + # 将提取的样本保存到输出文件 + with open(output_file_path, "w", encoding="utf-8") as f: + json.dump(filtered_samples, f, ensure_ascii=False, indent=4) + + print("过滤完成!") + print(f"成功提取的样本数: {len(filtered_samples)}") + print(f"结果已保存到: {output_file_path}") + + return True + + except json.JSONDecodeError as e: + print(f"错误: JSON文件解析失败 - {e}") + return False + except Exception as e: + print(f"错误: {e}") + return False + + +if __name__ == "__main__": + # 指定要处理的JSON文件路径 + benchmark_file_path = "./1223/benchmark_results-20251224-223035.json" + data_file_path = "./data/results/test/olympics/synthesis_data.json" + output_file_path = "./data/results/test/olympics/synthesis_data_zero.json" + + # 执行过滤操作 + filter_zero_recall_samples(benchmark_file_path, data_file_path, output_file_path) diff --git a/benchmark/script/merge_data.py b/benchmark/script/merge_data.py new file mode 100644 index 0000000..8d05ac6 --- /dev/null +++ b/benchmark/script/merge_data.py @@ -0,0 +1,96 @@ +import json +from pathlib import Path + + +def verify_no_duplicates(file_path): + """ + 验证文件中的数据没有重复 + + Args: + file_path: 要验证的文件路径 + + Returns: + bool: True表示没有重复，False表示有重复 + """ + with open(file_path, "r", encoding="utf-8") as f: + data = json.load(f) + + seen_questions = set() + duplicates = [] + + for item in data: + question = item.get("question", "") + if question in seen_questions: + duplicates.append(question) + else: + seen_questions.add(question) + + if duplicates: + print(f"\n警告: 发现 {len(duplicates)} 个重复的问题条目:") + for i, dup in enumerate(duplicates[:5], 1): + print(f" {i}. {dup[:100]}...") + return False + else: + print("\n验证通过: 文件中没有任何重复数据") + return True + + +def merge_and_deduplicate(input_dir, output_file): + """ + 合并目录下的所有JSON文件并去重 + + Args: + input_dir: 输入目录路径 + output_file: 输出文件路径 + """ + input_path = Path(input_dir) + all_data = [] + seen_questions = set() + + # 获取所有JSON文件 + json_files = sorted(input_path.glob("*.json")) + + print(f"找到 {len(json_files)} 个JSON文件:") + for f in json_files: + print(f" - {f.name}") + + # 读取并合并所有文件 + for json_file in json_files: + print(f"\n处理文件: {json_file.name}") + with open(json_file, "r", encoding="utf-8") as f: + data = json.load(f) + + if isinstance(data, list): + original_count = len(data) + for item in data: + # 使用question作为去重键 + question = item.get("question", "") + if question and question not in seen_questions: + seen_questions.add(question) + all_data.append(item) + + # 统计去重情况 + unique_count = sum(1 for item in data if item.get("question", "") in seen_questions) + print(f" 原始条目: {original_count}, 新增唯一条目: {unique_count}") + else: + print(f" 警告: {json_file.name} 不是数组格式，跳过") + + print("\n总计:") + print(f" 合并后总条目数: {len(all_data)}") + print(f" 去重后唯一Question数: {len(seen_questions)}") + + # 保存合并后的结果 + with open(output_file, "w", encoding="utf-8") as f: + json.dump(all_data, f, ensure_ascii=False, indent=2) + + print(f"\n结果已保存到: {output_file}") + + +if __name__ == "__main__": + input_dir = "./data/results/test/olympics" + output_file = "./data/results/test/olympics/merged_data.json" + + merge_and_deduplicate(input_dir, output_file) + + # 验证输出文件没有重复数据 + verify_no_duplicates(output_file) diff --git a/benchmark/script/stat_result.py b/benchmark/script/stat_result.py new file mode 100644 index 0000000..0c1e775 --- /dev/null +++ b/benchmark/script/stat_result.py @@ -0,0 +1,23 @@ +import json +import matplotlib.pyplot as plt + +# 加载数据 +with open("./1223/benchmark_results-20251225-220322.json", "r", encoding="utf-8") as f: + data = json.load(f) +recall_list = [item["recall"] for item in data["details"]] +print("召回率列表:", recall_list) +print("The number of samples:", len(recall_list)) +print("Recall Average:", sum(recall_list) / len(recall_list)) +print("The number of 0.0 in Recall List", recall_list.count(0.0)) + +# 绘制直方图 +plt.figure(figsize=(8, 5)) +plt.hist(recall_list, bins=20, range=(0, 1), edgecolor="black", alpha=0.75) +plt.xlabel("Recall") +plt.ylabel("Samples") +plt.title("Recall Distribution") +plt.grid(axis="y", linestyle="--", alpha=0.5) +plt.tight_layout() +plt.show() +plt.savefig("recall_hist.png", dpi=200) +print("已生成 recall_hist.png，请下载查看。") diff --git a/benchmark/test/test_ck.py b/benchmark/test/test_ck.py new file mode 100644 index 0000000..9761ed8 --- /dev/null +++ b/benchmark/test/test_ck.py @@ -0,0 +1,73 @@ +import os +import pandas as pd +from clickhouse_connect import get_client + + +def get_myscale_client(): + """创建并返回 MyScaleDB 客户端""" + return get_client( + host=os.getenv("MYSCALE_HOST"), # 你的MyScale IP + port=int(os.getenv("MYSCALE_PORT")), # HTTP客户端专用端口 + user=os.getenv("MYSCALE_USER"), # 用户名 + password=os.getenv("MYSCALE_PASSWORD"), # 密码 + database=os.getenv("MYSCALE_DATABASE"), # 默认数据库 + ) + + +def run_myscale_sql(sql: str, return_df: bool = True): + """ + 执行 MyScale SQL 并返回结果（终极兼容版） + :param sql: 要执行的SQL语句 + :param return_df: 是否返回DataFrame（False返回原生结果） + :return: 执行结果（DataFrame/原生结果） + """ + client = None + try: + # 创建客户端 + client = get_myscale_client() + print(f"🔍 执行SQL: {sql}") + + # 执行SQL + result = client.query(sql) + + # 处理结果（终极兼容：适配所有clickhouse-connect版本） + if return_df: + # 无结果集（DDL语句） + if not result.result_set: + print("✅ 执行成功！无返回数据（DDL语句）") + return None + # 有结果集：直接用column_names作为列名（兼容字符串列表格式） + else: + # 核心修复：column_names本身就是字符串列表，无需解析字典 + columns = result.column_names + data = result.result_set + df = pd.DataFrame(data, columns=columns) + print(f"✅ 执行成功！返回 {len(df)} 行数据") + return df + else: + print("✅ 执行成功！") + return result + except Exception as e: + print(f"❌ SQL执行失败: {str(e)}") + raise + finally: + # 确保客户端连接关闭 + if client: + client.close() + + +# ========== 极简测试示例 ========== +if __name__ == "__main__": + # 测试1：查看所有表（最基础、最易成功的测试） + print("===== 测试1：查看数据库中所有表 =====") + sql1 = "SHOW TABLES" + df1 = run_myscale_sql(sql1) + if df1 is not None: + print(df1) + + # 测试2：执行简单查询（验证数据返回） + print("\n===== 测试2：执行简单计数查询 =====") + sql2 = "SELECT 1 as test_col, 'hello' as test_str" + df2 = run_myscale_sql(sql2) + if df2 is not None: + print(df2) diff --git a/benchmark/test/test_dify.py b/benchmark/test/test_dify.py new file mode 100644 index 0000000..2a1eed3 --- /dev/null +++ b/benchmark/test/test_dify.py @@ -0,0 +1,62 @@ +import requests +import json + +# 核心配置（仅需替换 API Key，其他无需改） +API_KEY = "app-O1vzdkyNbfYBrG4aDjHb0VQl" +DIFY_URL = "https://api.dify.ai/v1/chat-messages" + + +def get_agent_answer(question): + """调用 Dify Agent 模型，返回完整回答""" + # 请求参数（Agent 模型必须用 streaming 模式） + payload = { + "inputs": {}, # 必填字段（空字典即可） + "query": question, + "response_mode": "streaming", + "user": "test_user", + } + headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} + + try: + # 发送无代理流式请求 + resp = requests.post( + DIFY_URL, + headers=headers, + json=payload, + stream=True, + timeout=15, # 超时保护 + ) + resp.raise_for_status() # 有 HTTP 错误直接抛出 + + # 提取并拼接完整回答（仅保留核心 agent_message 事件） + full_answer = "" + for line in resp.iter_lines(chunk_size=512): # 小分片读取，提升稳定性 + if not line: + continue + line_str = line.decode("utf-8").strip() + # 只处理包含核心回答的行，过滤无关事件 + if line_str.startswith("data: ") and "agent_message" in line_str: + try: + data = json.loads(line_str[6:]) # 去掉 "data: " 前缀 + full_answer += data.get("answer", "") + except Exception: + continue # 忽略解析失败的行（不影响整体） + + return full_answer + + except Exception as e: + return f"❌ 调用失败：{str(e)[:100]}" + + +# ========== 测试运行 ========== +if __name__ == "__main__": + # 测试问题 + test_question = "Find the top 5 papers about machine learning in natural language processing" + + print("🔄 调用 Dify Agent 模型（无代理）...") + answer = get_agent_answer(test_question) + + print("\n✅ 完整回答：") + print("-" * 60) + print(answer if answer else "⚠️ 未提取到回答") + print("-" * 60) diff --git a/benchmark/test/test_dify_response.py b/benchmark/test/test_dify_response.py new file mode 100644 index 0000000..0208b77 --- /dev/null +++ b/benchmark/test/test_dify_response.py @@ -0,0 +1,49 @@ +import json +import requests + +API_KEY = "app-O1vzdkyNbfYBrG4aDjHb0VQl" +DIFY_URL = "https://api.dify.ai/v1/chat-messages" + + +def test_dify_response(): + """测试Dify API的实际响应格式""" + payload = { + "inputs": {}, + "query": "列出数据库中所有的表", + "response_mode": "streaming", + "user": "test_user", + } + headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} + + try: + resp = requests.post(DIFY_URL, headers=headers, json=payload, stream=True, timeout=30) + resp.raise_for_status() + + print("=" * 80) + print("原始流式响应数据:") + print("=" * 80) + + for i, line in enumerate(resp.iter_lines(chunk_size=512)): + if not line: + continue + line_str = line.decode("utf-8").strip() + print(f"\n[Line {i + 1}]") + print(line_str) + + # 尝试解析JSON + if line_str.startswith("data: "): + try: + data = json.loads(line_str[6:]) + print(f" -> 解析后的event: {data.get('event', 'N/A')}") + print(f" -> 包含answer字段: {'answer' in data}") + if "answer" in data: + print(f" -> answer内容: {data['answer'][:100]}") + except Exception as e: + print(f" -> JSON解析失败: {e}") + + except Exception as e: + print(f"ERROR: {str(e)}") + + +if __name__ == "__main__": + test_dify_response() diff --git a/benchmark/test/test_full_answer.py b/benchmark/test/test_full_answer.py new file mode 100644 index 0000000..9ea4115 --- /dev/null +++ b/benchmark/test/test_full_answer.py @@ -0,0 +1,72 @@ +import json +import requests + +API_KEY = "app-O1vzdkyNbfYBrG4aDjHb0VQl" +DIFY_URL = "https://api.dify.ai/v1/chat-messages" + + +def get_full_answer(question: str): + """获取完整答案""" + payload = { + "inputs": {}, + "query": question, + "response_mode": "streaming", + "user": "benchmark_user", + } + headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} + + try: + resp = requests.post(DIFY_URL, headers=headers, json=payload, stream=True, timeout=60) + resp.raise_for_status() + + full_answer = "" + has_error = False + error_message = "" + + for line in resp.iter_lines(chunk_size=512): + if not line: + continue + line_str = line.decode("utf-8").strip() + + if line_str.startswith("data: "): + try: + data = json.loads(line_str[6:]) + event_type = data.get("event", "") + + if event_type == "error": + has_error = True + error_message = data.get("message", "Unknown error") + break + + if "answer" in data: + full_answer += data.get("answer", "") + + except json.JSONDecodeError: + continue + + if has_error: + return f"ERROR: {error_message}" + + return full_answer + + except Exception as e: + return f"ERROR: {str(e)}" + + +if __name__ == "__main__": + question = ( + "Hey! Could you give me the list of all the article titles you've got in the database?" + ) + print(f"查询: {question}\n") + answer = get_full_answer(question) + print(f"答案长度: {len(answer)} 字符") + print(f"\n完整答案:\n{answer}") + + # 检查是否包含标准答案 + standard_answers = ["Test Article 0", "Test Article 1", "Test Article 2"] + print("\n\n检查标准答案:") + for ans in standard_answers: + if ans in answer: + print(f" ✅ 找到: {ans}") + else: + print(f" ❌ 未找到: {ans}") diff --git a/benchmark/test/test_simple_query.py b/benchmark/test/test_simple_query.py new file mode 100644 index 0000000..db5cb60 --- /dev/null +++ b/benchmark/test/test_simple_query.py @@ -0,0 +1,75 @@ +import os +import json +import requests +from dotenv import load_dotenv + +# 加载环境变量 +load_dotenv() + +API_KEY = os.getenv("API_KEY") +DIFY_URL = os.getenv("DIFY_URL", "https://api.dify.ai/v1/chat-messages") + + +def get_dify_answer_detailed(question: str): + """调用 Dify Agent 并打印详细的流程信息""" + payload = { + "inputs": {}, + "query": question, + "response_mode": "streaming", + "user": "benchmark_user", + } + headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} + + try: + resp = requests.post(DIFY_URL, headers=headers, json=payload, stream=True, timeout=60) + resp.raise_for_status() + + print("=" * 80) + print(f"查询: {question}") + print("=" * 80) + + for line in resp.iter_lines(chunk_size=512): + if not line: + continue + line_str = line.decode("utf-8").strip() + + if line_str.startswith("data: "): + try: + data = json.loads(line_str[6:]) + event_type = data.get("event", "") + + print(f"\n[Event: {event_type}]") + + if event_type == "agent_thought": + print(f" Thought: {data.get('thought', '')}") + print(f" Tool: {data.get('tool', '')}") + print(f" Tool Input: {data.get('tool_input', '')}") + + elif event_type == "agent_message": + print(f" Answer: {data.get('answer', '')}") + + elif event_type == "message": + print(f" Answer: {data.get('answer', '')}") + + elif event_type == "error": + print(f" ❌ Error: {data.get('message', '')}") + print(f" Code: {data.get('code', '')}") + + elif event_type == "tool": + print(f" Tool: {data.get('tool_name', '')}") + print(f" Tool Output: {str(data.get('tool_output', ''))[:200]}") + + except json.JSONDecodeError as e: + print(f" JSON解析错误: {e}") + + except Exception as e: + print(f"ERROR: {str(e)}") + + +if __name__ == "__main__": + # 测试几个不同的查询 + queries = ["列出数据库中所有的表", "Articles表有多少条记录？", "给我返回一篇文章的标题"] + + for q in queries: + get_dify_answer_detailed(q) + print("\n\n") diff --git a/benchmark/tools/common.py b/benchmark/tools/common.py new file mode 100644 index 0000000..91964f8 --- /dev/null +++ b/benchmark/tools/common.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Common utilities for the Text2VectorSQL Benchmark +""" + +import re + + +def get_dify_answer(question: str, api_key: str, dify_url: str) -> str: + """ + 调用 Dify API 获取回答 + + Args: + question: 自然语言问题 + api_key: Dify API 密钥 + dify_url: Dify API URL + + Returns: + Dify 回答内容 + """ + payload = { + "inputs": {}, + "query": question, + "response_mode": "streaming", + "user": "benchmark_user", + } + headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} + + try: + import requests + import json + + resp = requests.post(dify_url, headers=headers, json=payload, stream=True, timeout=60) + resp.raise_for_status() + + full_answer = "" + for line in resp.iter_lines(chunk_size=512, decode_unicode=True): + if not line: + continue + line = line.strip() + if line.startswith("data: "): + try: + chunk = json.loads(line[6:]) + if chunk.get("event") in {"message", "agent_message", "agent_thought"}: + full_answer += chunk.get("answer") or chunk.get("thought") or "" + except json.JSONDecodeError: + continue + return full_answer or "ERROR: empty answer" + + except Exception as e: + return f"ERROR: {str(e)}" + + +def unify_lembed_clauses(standard_sql, predicted_sql): + """ + 统一标准SQL和预测SQL的lembed子句 + + Args: + standard_sql: 标准SQL语句 + predicted_sql: 预测SQL语句 + + Returns: + 统一后的预测SQL语句 + """ + # 改进的lembed子句提取模式，能够处理包含单引号的文本 + # 匹配模式：lembed(模型名, 文本内容)，支持单引号和双引号 + lembed_pattern = r'lembed\s*\(\s*([^,]+?),\s*(["\'])(.*?)\2\s*\)' + + # 提取标准SQL中的lembed子句 + standard_matches = list(re.finditer(lembed_pattern, standard_sql, re.DOTALL)) + # 提取预测SQL中的lembed子句 + predicted_matches = list(re.finditer(lembed_pattern, predicted_sql, re.DOTALL)) + + # 如果两者都只有一句lembed子句，则进行替换 + if len(standard_matches) == 1 and len(predicted_matches) == 1: + # 提取标准SQL的lembed子句信息 + standard_match = standard_matches[0] + standard_text = standard_match.group(3) + + # 提取预测SQL的lembed子句信息 + predicted_match = predicted_matches[0] + predicted_model = predicted_match.group(1).strip() + predicted_quote = predicted_match.group(2) # 预测SQL使用的引号类型 + predicted_lembed_full = predicted_match.group(0) + + # 处理文本中的引号：如果标准文本中包含预测SQL使用的引号，则转义 + if predicted_quote in standard_text: + # 转义标准文本中的引号 + escaped_text = standard_text.replace(predicted_quote, f"\\{predicted_quote}") + else: + escaped_text = standard_text + + # 构建统一后的lembed子句：使用标准SQL的文本内容，预测SQL的模型名和引号类型 + unified_lembed = ( + f"lembed({predicted_model}, {predicted_quote}{escaped_text}{predicted_quote})" + ) + + # 将预测SQL中的lembed子句替换为统一后的lembed子句 + unified_sql = predicted_sql.replace(predicted_lembed_full, unified_lembed) + + return unified_sql + + return predicted_sql diff --git a/benchmark/tools/deepseek_sql_rewrite.py b/benchmark/tools/deepseek_sql_rewrite.py new file mode 100755 index 0000000..32256d9 --- /dev/null +++ b/benchmark/tools/deepseek_sql_rewrite.py @@ -0,0 +1,368 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +使用DeepSeek API批量改写SQL并执行的脚本 + +功能特性： +- ✅ 批量处理：支持从文件读取多个SQL语句 +- ✅ JSON格式：支持从JSON文件提取SQL语句 +- ✅ 交互式输入：支持手动输入SQL语句 +- ✅ 环境变量：支持通过环境变量设置配置 +- ✅ 批量执行：自动执行所有改写后的SQL +- ✅ 结果展示：详细展示每个SQL的处理结果 + +使用示例： +1. 从普通文件批量处理（分号分隔）： + python deepseek_sql_rewrite.py --sqls sql_file.txt --prompt "请帮我优化这些SQL查询" + +2. 从JSON文件批量处理： + python deepseek_sql_rewrite.py --sqls json_file.json --prompt "请帮我将这些SQL改写为带Rerank的SQL" + +3. 交互式输入： + python deepseek_sql_rewrite.py + 请输入SQL语句（输入'GO'结束，支持多行输入）: + SELECT * FROM city LIMIT 10 + GO + +文件格式支持： + +1. 普通SQL文件格式（分号分隔）： + SELECT * FROM city LIMIT 10; + SELECT * FROM event WHERE year > 2020; + SELECT city_name FROM city WHERE country = 'China'; + +2. JSON文件格式（数组）： + [ + { + "question": "问题1", + "sql": "SELECT * FROM city LIMIT 10;" + }, + { + "question": "问题2", + "sql": "SELECT * FROM event WHERE year > 2020;" + } + ] + +3. JSON文件格式（单个对象）： + { + "question": "问题", + "sql": "SELECT * FROM city LIMIT 10;" + } +""" + +import os +import json +import requests +import argparse +from clickhouse_connect import get_client +from typing import List, Tuple + + +def call_deepseek_api(original_sql: str) -> str: + """ + 调用DeepSeek API改写SQL + + Args: + original_sql: 原始SQL语句 + prompt: 提示词 + api_key: DeepSeek API密钥 + + Returns: + 改写后的SQL语句 + """ + prompt = """因为向量查询精度不高，我需要给我的sql按照我的语法，加上rerank方法。 + 1. 例如例如将vector sql：WITH + lembed('intfloat/E5-Mistral-7B-Instruct', 'bustling, vibrant, cultural, and have historical sites') AS ref_vec + +SELECT + city_name, + city_description, + distance(city_description_embedding, ref_vec) AS distance +FROM + olympics.city +ORDER BY + distance +LIMIT 3;改写成这种rerank的格式：WITH + -- 步骤 1：缓存带行号的原始候选集 + candidate_set_with_rownum AS ( + SELECT + -- 生成连续行号（从 1 开始），与 Rerank 返回的行号一一对应 + rowNumberInAllBlocks() AS candidate_rownum, + city_name, + city_description -- 可添加其他需要的原始字段 + FROM ( + SELECT + city_name, + city_description + FROM olympics.city + ORDER BY distance( + city_description_embedding, + lembed('intfloat/E5-Mistral-7B-Instruct', 'A bustling metropolis with lots of cultural landmarks') + ) + LIMIT 10 -- 必须与 Rerank 中的候选集 LIMIT 保持一致，确保行号匹配 + ) + ) +-- 步骤 2：提取 Rerank 行号 + 关联原始数据（无缝衔接你已验证成功的逻辑） +SELECT + -- Rerank 解析结果 + tupleElement(single_rerank_tuple, 1) AS rerank_seq_num, -- Rerank 返回的行号 + tupleElement(single_rerank_tuple, 3) AS relevance_score, -- 可选：Rerank 相关性得分（用于排序） + -- 原始表数据（从候选集中关联提取） + cs.city_name, + cs.city_description +FROM + ( + -- 你的原始 Rerank 调用（已验证成功，无需修改） + SELECT + Rerank( + 'A bustling metropolis with lots of cultural landmarks', + ( + SELECT groupArray(city_description) + FROM ( + SELECT city_description + FROM olympics.city + ORDER BY distance( + city_description_embedding, + lembed('intfloat/E5-Mistral-7B-Instruct', 'A bustling metropolis with lots of cultural landmarks') + ) + LIMIT 10 + ) + ), + 5, --最后需要返回的行数，也就是limit k,重写你需要按照原始sql更改这个值 + 'Cohere', + 'https://api.cohere.ai/v1/rerank', + 'FtrW8La5zmyq05H14x6JV2vbYGBrFG9ILyAH3iI7', + '{"model": "rerank-multilingual-v3.0", "top_k": 20}' + ) AS reranked_result + ) AS rerank_data +-- 步骤 3：展开 Rerank 集合（你已验证成功的 ARRAY JOIN） +ARRAY JOIN reranked_result AS single_rerank_tuple +-- 步骤 4：通过行号关联原始候选集（核心：匹配 Rerank 行号与原始候选集行号） +JOIN candidate_set_with_rownum cs + ON cs.candidate_rownum = tupleElement(single_rerank_tuple, 1) +-- 可选：按 Rerank 得分降序排序（得到最终重排序结果） +ORDER BY relevance_score DESC; +2. 改写后需要检查一下sql里表的别名对不对，能不能使用。 +3. 改写后sql返回的数据格式，也就是列名与行数（limit k值），需要与原始sql一致。 +4. 你需要注意，如果sql中嵌套了向量查询的子句，你也需要按照语义分别改写向量查询的子句为上面的rerank格式。 +""" + url = os.getenv("LLM_API_URL", "https://api.deepseek.com/chat/completions") + api_key = os.getenv("LLM_API_KEY") + headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"} + + messages = [ + { + "role": "user", + "content": f"{prompt}\n\n原始SQL: {original_sql}\n\n请只返回改写后的可执行的SQL语句，不要包含其他多余的字符。", + } + ] + + data = { + "model": "deepseek-chat", + "messages": messages, + "temperature": 0.1, + "max_tokens": 1000, + "stream": False, + } + + try: + response = requests.post(url, headers=headers, json=data) + response.raise_for_status() + + result = response.json() + if "choices" in result and len(result["choices"]) > 0: + return result["choices"][0]["message"]["content"].strip()[ + 7:-5 + ] # 需要去掉开头的‘’‘sql 和末尾的‘’‘和分号和一个空格 + else: + raise ValueError("DeepSeek API返回格式错误") + + except requests.RequestException as e: + print(f"DeepSeek API调用失败: {str(e)}") + return "" + except Exception as e: + print(f"DeepSeek API处理失败: {str(e)}") + return "" + + +def run_sql_with_columns( + sql: str, host: str, port: int, user: str, password: str, database: str +) -> Tuple[List[tuple], List[str]]: + """ + 执行SQL查询并返回结果和列名 + + Args: + sql: SQL查询语句 + host: 数据库主机 + port: 数据库端口 + user: 数据库用户名 + password: 数据库密码 + database: 数据库名称 + + Returns: + (查询结果数据, 查询结果列名) + """ + client = None + try: + client = get_client(host=host, port=port, user=user, password=password, database=database) + + result = client.query(sql) + + if not result.result_set: + return [], [] + + column_names = result.column_names + + # 过滤掉distance和embedding字段（可选） + distance_indices = [i for i, col in enumerate(column_names) if "distance" in col.lower()] + embedding_indices = [i for i, col in enumerate(column_names) if "embedding" in col.lower()] + exclude_indices = set(distance_indices + embedding_indices) + + data = [] + for row in result.result_set: + filtered_row = tuple( + value for idx, value in enumerate(row) if idx not in exclude_indices + ) + data.append(filtered_row) + + filtered_columns = [ + col for idx, col in enumerate(column_names) if idx not in exclude_indices + ] + + return data, filtered_columns + + except Exception as e: + print(f"SQL执行失败: {str(e)}") + return [], [] + finally: + if client: + client.close() + + +def main(): + """ + 主函数 + """ + # 默认配置（来自用户提供的信息） + DEFAULT_DEEPSEEK_API_KEY = os.getenv("LLM_API_KEY") + DEFAULT_MYSCALE_HOST = os.getenv("MYSCALE_HOST") + DEFAULT_MYSCALE_PORT = int(os.getenv("MYSCALE_PORT")) + DEFAULT_MYSCALE_USER = os.getenv("MYSCALE_USER") + DEFAULT_MYSCALE_PASSWORD = os.getenv("MYSCALE_PASSWORD") + DEFAULT_MYSCALE_DATABASE = os.getenv("MYSCALE_DATABASE") + DEFAULT_SQL_PATH = "./data/results/test/olympics/olympics_qs.json" + parser = argparse.ArgumentParser(description="使用DeepSeek API批量改写SQL并执行") + parser.add_argument( + "--sqls", help="包含多个SQL语句的文件名（使用;分隔）", default=DEFAULT_SQL_PATH + ) + parser.add_argument("--api-key", help="DeepSeek API密钥", default=DEFAULT_DEEPSEEK_API_KEY) + parser.add_argument("--host", help="数据库主机", default=DEFAULT_MYSCALE_HOST) + parser.add_argument("--port", help="数据库端口", type=int, default=DEFAULT_MYSCALE_PORT) + parser.add_argument("--user", help="数据库用户名", default=DEFAULT_MYSCALE_USER) + parser.add_argument("--password", help="数据库密码", default=DEFAULT_MYSCALE_PASSWORD) + parser.add_argument("--database", help="数据库名称", default=DEFAULT_MYSCALE_DATABASE) + + args = parser.parse_args() + + sql_statements = [] + + # 处理文件输入 + if args.sqls: + try: + with open(args.sqls, "r", encoding="utf-8") as f: + file_content = f.read().strip() + + # 检测是否为JSON格式 + if file_content.startswith("{") or file_content.startswith("["): + # JSON格式解析 + json_data = json.loads(file_content) + sql_statements = [] + + # 处理数组格式 [{}, {}, ...] + if isinstance(json_data, list): + for item in json_data: + if isinstance(item, dict) and "sql" in item: + sql_statements.append(item["sql"].strip()) + # 处理单个对象格式 {} + elif isinstance(json_data, dict) and "sql" in json_data: + sql_statements.append(json_data["sql"].strip()) + else: + print("JSON格式不正确，无法提取SQL语句") + return + else: + # 普通分号分隔格式解析 + sql_statements = file_content.split(";") + # 过滤掉空语句 + sql_statements = [sql.strip() for sql in sql_statements if sql.strip()] + + except json.JSONDecodeError as e: + print(f"JSON解析失败: {str(e)}") + return + except Exception as e: + print(f"读取文件失败: {str(e)}") + return + else: + # 处理交互式输入 + print("请输入SQL语句（输入'GO'结束，支持多行输入）:") + sql_lines = [] + while True: + line = input().strip() + if line.upper() == "GO": + break + sql_lines.append(line) + sql_text = " ".join(sql_lines).strip() + if sql_text: + sql_statements = [sql_text] + + if not sql_statements: + print("没有有效的SQL语句可以处理") + return + + if not args.api_key: + print("请提供DeepSeek API密钥，可以使用 --api-key 参数或者设置DEEPSEEK_API_KEY环境变量") + return + + print("=== 开始批量处理 ===") + print(f"总共要处理 {len(sql_statements)} 个SQL语句\n") + + # 循环处理每个SQL语句 + for idx, sql in enumerate(sql_statements[40:50], 1): + print( + f"\n==================== 处理第 {idx}/{len(sql_statements)} 个SQL ====================" + ) + print(f"原始SQL: {sql}") + + # 调用DeepSeek API改写SQL + print("\n正在调用DeepSeek API改写SQL...") + rewritten_sql = call_deepseek_api(sql) + + if not rewritten_sql: + print("改写失败，跳过此SQL") + continue + print("*" * 80) + print(f"改写后的SQL: {rewritten_sql}") + print("*" * 80) + # 执行改写后的SQL + print("\n正在执行改写后的SQL...") + data, columns = run_sql_with_columns( + rewritten_sql, args.host, args.port, args.user, args.password, args.database + ) + + if data and columns: + print("\n=== 执行结果 ===") + print(f"返回列名: {columns}") + print(f"返回行数: {len(data)}") + print("\n结果示例:") + # 打印前5行 + for i, row in enumerate(data[:5]): + print(f" 行{i + 1}: {row}") + if len(data) > 5: + print(f" ... 还有{len(data) - 5}行") + else: + print("\n执行结果为空或执行失败") + + print("\n=== 批量处理完成 ===") + + +if __name__ == "__main__": + main() diff --git a/benchmark/tools/hybrid_search.py b/benchmark/tools/hybrid_search.py new file mode 100644 index 0000000..e639ffb --- /dev/null +++ b/benchmark/tools/hybrid_search.py @@ -0,0 +1,142 @@ +import re + + +def rewrite_single_table_to_hybrid_search(sql): + """ + 单表 SQL 改写为 HybridSearch 格式（仅处理纯向量检索的单表 SQL） + :param sql: 原始单表向量检索 SQL + :return: 改写后的 HybridSearch 格式 SQL + """ + # 步骤 1：提取关键信息（通过正则匹配） + # 匹配 lembed 中的模型和查询语义 + lembed_pattern = r"lembed\(\'([^\']+)\', \'([^\']+)\'\)" + lembed_match = re.search(lembed_pattern, sql) + if not lembed_match: + return sql # 无 lembed 语法，返回原 SQL + + model_name = lembed_match.group(1) + query_text = lembed_match.group(2) + + # 匹配 distance 函数中的向量字段 + distance_pattern = r"distance\(([^,]+), [^\)]+\)" + distance_match = re.search(distance_pattern, sql) + if not distance_match: + return sql # 无 distance 语法，返回原 SQL + + embedding_field = distance_match.group(1).strip() + # 推导文本字段（向量字段去除 _embedding 后缀，如 city_description_embedding → city_description） + text_field = embedding_field.replace("_embedding", "") + if text_field == embedding_field: + # 若无 _embedding 后缀，默认与向量字段同名（兜底方案） + text_field = embedding_field + + # 匹配表名（FROM 后的表名） + from_pattern = r"FROM\s+([^\s]+)" + from_match = re.search(from_pattern, sql, re.IGNORECASE) + if not from_match: + return sql + + table_name = from_match.group(1).strip() + # 处理表别名（如 city AS c → city） + table_name = re.sub(r"\s+AS\s+[^\s]+", "", table_name, flags=re.IGNORECASE) + + # 匹配 SELECT 字段（保留原查询字段，去除 distance 相关字段） + select_pattern = r"SELECT\s+(.*?)\s+FROM" + select_match = re.search(select_pattern, sql, re.DOTALL | re.IGNORECASE) + if not select_match: + return sql + + select_fields = select_match.group(1).strip() + # 移除原 distance 字段（避免重复） + select_fields = re.sub( + r"distance\([^)]+\)\s+AS\s+distance\s*,?\s*", "", select_fields, flags=re.IGNORECASE + ) + # 移除末尾多余的逗号 + select_fields = re.sub(r",\s*$", "", select_fields) + if not select_fields: + select_fields = "*" # 兜底：若 SELECT 字段为空，使用 * + + # 匹配 LIMIT 数值 + limit_pattern = r"LIMIT\s+(\d+)" + limit_match = re.search(limit_pattern, sql, re.IGNORECASE) + limit_num = limit_match.group(1) if limit_match else "5" + + # 步骤 2：构造 HybridSearch 语句 + hybrid_search_clause = f""" + HybridSearch( + 'fusion_type=RSF', + 'fusion_weight=0.4' + )( + {embedding_field}, + {text_field}, + lembed('{model_name}', '{query_text}'), + '{query_text}' + ) AS score + """ + + # 步骤 3：拼接最终 SQL（去除原 WITH 子句和 ORDER BY distance） + final_sql = f"""SELECT + {select_fields}, + {hybrid_search_clause} +FROM {table_name} +ORDER BY score DESC +LIMIT {limit_num};""" + + # 格式化 SQL（去除多余空行和空格，提升可读性） + final_sql = re.sub(r"\n\s+", "\n ", final_sql) + final_sql = re.sub(r"\s+", " ", final_sql).strip() + final_sql = final_sql.replace(";", "\n;") + + return final_sql + + +def process_sql_list(sql_list): + """ + 批量处理 SQL 列表，单表改写，多表返回原文 + :param sql_list: 原始 SQL 字典列表（格式与用户输入一致） + :return: 处理后的 SQL 结果列表 + """ + processed_results = [] + for item in sql_list: + original_sql = item.get("sql", "") + question = item.get("question", "") + + # 判断是否为多表查询：包含 JOIN 关键字即为多表，直接返回原 SQL + if "JOIN" in original_sql.upper(): + processed_sql = original_sql + else: + # 单表查询：改写为 HybridSearch 格式 + processed_sql = rewrite_single_table_to_hybrid_search(original_sql) + + # 构造结果项 + processed_results.append( + {"question": question, "original_sql": original_sql, "processed_sql": processed_sql} + ) + + return processed_results + + +# ---------------------- 示例使用 ---------------------- +if __name__ == "__main__": + # 你的原始 SQL 数据（可直接替换为完整数据） + sample_sql_data = [ + { + "question": "Could you show me the 5 cities that are most representative of a capital city with a rich history and vibrant culture?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'Capital city with rich history and vibrant culture') AS ref_vec_0\n\nSELECT city_name, distance(city.city_description_embedding, ref_vec_0) AS distance\nFROM city\nORDER BY distance\nLIMIT 5;", + }, + { + "question": "Could you show me the top 5 cities that hosted games most associated with winter conditions in a snowy region, and list their names and IDs?", + "sql": "WITH\n lembed('all-MiniLM-L6-v2', 'The Winter games occurred in a snowy region.') AS ref_vec_0,\n\nRankedGames AS (\n SELECT \n g.id AS games_id, \n g.games_name AS games_name, \n g.games_year AS games_year, \n g.season AS season, \n distance(g.games_description_embedding, ref_vec_0) AS games_distance\n FROM games AS g\n ORDER BY games_distance\n LIMIT 5\n),\n\nCityGames AS (\n SELECT \n c.id AS city_id, \n c.city_name AS city_name, \n cg.games_id AS games_id\n FROM city AS c\n JOIN games_city AS cg ON toString(c.id) = toString(cg.city_id)\n WHERE cg.games_id IN (SELECT games_id FROM RankedGames)\n)\n\nSELECT \n c.city_id AS city_id, \n c.city_name AS city_name\nFROM CityGames AS c\nJOIN RankedGames AS rg ON toString(c.games_id) = toString(rg.games_id)\nORDER BY rg.games_distance\nLIMIT 5;", + }, + ] + + # 批量处理 SQL + result = process_sql_list(sample_sql_data) + + # 打印处理结果 + for idx, item in enumerate(result, 1): + print(f"===== 第 {idx} 条结果 =====") + print(f"问题：{item['question'][:50]}...") + print(f"\n原始 SQL：\n{item['original_sql'][:100]}...") + print(f"\n处理后 SQL：\n{item['processed_sql']}") + print("-" * 80 + "\n") diff --git a/benchmark/tools/parse_log.py b/benchmark/tools/parse_log.py new file mode 100644 index 0000000..bc21245 --- /dev/null +++ b/benchmark/tools/parse_log.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +解析日志文件，提取每个样本的相关信息 +""" + +import re +import json +from typing import Dict, List, Any + + +def parse_log_file(log_path: str) -> List[Dict[str, Any]]: + """ + 解析日志文件，提取每个样本的信息 + + Args: + log_path: 日志文件路径 + + Returns: + 包含每个样本信息的列表 + """ + with open(log_path, "r", encoding="utf-8") as f: + content = f.read() + + samples = [] + + # 使用样本标记分割样本 + sample_pattern = r"📝 样本 (\d+)/(\d+)" + sample_matches = list(re.finditer(sample_pattern, content)) + + for i in range(len(sample_matches)): + start = sample_matches[i].end() + end = sample_matches[i + 1].start() if i < len(sample_matches) - 1 else len(content) + sample_content = content[start:end] + + # 提取问题 + question_pattern = r"问题: (.+?)\s*\n\n" + question_match = re.search(question_pattern, sample_content, re.DOTALL) + question = question_match.group(1).strip() if question_match else None + + # 提取标准SQL + standard_sql_pattern = r" 标准SQL: ([\s\S]+?)\s* 预测SQL:" + standard_sql_match = re.search(standard_sql_pattern, sample_content) + standard_sql = standard_sql_match.group(1).strip() if standard_sql_match else None + + # 提取预测SQL + predicted_sql_pattern = r" 预测SQL: ([\s\S]+?)\s* 步骤3:" + predicted_sql_match = re.search(predicted_sql_pattern, sample_content) + predicted_sql = predicted_sql_match.group(1).strip() if predicted_sql_match else None + + # 提取test_values + test_values_pattern = r"test_values: (.+?)\s*golden_values: " + test_values_match = re.search(test_values_pattern, sample_content, re.DOTALL) + test_values = test_values_match.group(1).strip() if test_values_match else None + + # 提取golden_values + golden_values_pattern = r"golden_values: (.+?)\s*intersection: " + golden_values_match = re.search(golden_values_pattern, sample_content, re.DOTALL) + golden_values = golden_values_match.group(1).strip() if golden_values_match else None + + # 提取intersection + intersection_pattern = r"intersection: (.+?)\s* ✅ 评估结果:" + intersection_match = re.search(intersection_pattern, sample_content, re.DOTALL) + intersection = intersection_match.group(1).strip() if intersection_match else None + + # 提取评估指标 + metrics_pattern = r" Exact Match: (\d+\.\d+)\s+Precision: (\d+\.\d+)\s+Recall: (\d+\.\d+)\s+F1: (\d+\.\d+)\s+MAP: (\d+\.\d+)\s+MRR: (\d+\.\d+)\s+NDCG: (\d+\.\d+)\s+LLM Overall: (\d+\.\d+)" + metrics_match = re.search(metrics_pattern, sample_content, re.DOTALL) + metrics = ( + { + "Exact Match": float(metrics_match.group(1)) if metrics_match else None, + "Precision": float(metrics_match.group(2)) if metrics_match else None, + "Recall": float(metrics_match.group(3)) if metrics_match else None, + "F1": float(metrics_match.group(4)) if metrics_match else None, + "MAP": float(metrics_match.group(5)) if metrics_match else None, + "MRR": float(metrics_match.group(6)) if metrics_match else None, + "NDCG": float(metrics_match.group(7)) if metrics_match else None, + "LLM Overall": float(metrics_match.group(8)) if metrics_match else None, + } + if metrics_match + else None + ) + + sample_info = { + "sample_id": i + 1, + "question": question, + "standard_sql": standard_sql, + "predicted_sql": predicted_sql, + "test_values": test_values, + "golden_values": golden_values, + "intersection": intersection, + "metrics": metrics, + } + + samples.append(sample_info) + + return samples + + +def main(): + log_path = "./log/CleanData/0119_100.log" + samples = parse_log_file(log_path) + + # 输出结果 + print(f"共提取到 {len(samples)} 个样本") + + # 打印前几个样本的信息 + for i, sample in enumerate(samples[:5]): + print(f"\n=== 样本 {sample['sample_id']} ===") + print( + f"问题: {sample['question'][:50]}..." + if sample["question"] and len(sample["question"]) > 50 + else f"问题: {sample['question']}" + ) + print(f"标准SQL: {'存在' if sample['standard_sql'] else 'None'}") + print(f"预测SQL: {'存在' if sample['predicted_sql'] else 'None'}") + print(f"test_values: {sample['test_values']}") + print(f"golden_values: {sample['golden_values']}") + print(f"intersection: {sample['intersection']}") + print(f"评估指标: {sample['metrics']}") + + # 保存为JSON文件 + output_path = "./tools/log_analysis_result.json" + with open(output_path, "w", encoding="utf-8") as f: + json.dump(samples, f, ensure_ascii=False, indent=2) + + print(f"\n结果已保存到: {output_path}") + + +if __name__ == "__main__": + main() diff --git a/mcp_server/text2vecsql/server.py b/mcp_server/text2vecsql/server.py index c7fd617..d6c5ab5 100644 --- a/mcp_server/text2vecsql/server.py +++ b/mcp_server/text2vecsql/server.py @@ -27,8 +27,19 @@ class TextToVecSQLResponse: @classmethod def handle_response(cls, response: requests.Response) -> "TextToVecSQLResponse": """Handle a response from the Text to Vector SQL server.""" - assert response.status_code == 200, f"Error: {response.json()['error_message']}" - results = response.json()["result"] + assert response.status_code == 200, ( + f"Error: {response.json()['error_message'] if 'error_message' in response.json() else str(response.json())}" + ) + + # 处理新的聊天完成 API 响应格式 + response_data = response.json() + if "choices" in response_data and response_data["choices"]: + # 从聊天完成 API 响应中提取 assistant 回复 + results = response_data["choices"][0]["message"]["content"] + else: + # 兼容旧格式 + results = response_data["result"] + handle_step = "" sql = "" next_is_sql = False @@ -69,18 +80,41 @@ class TextToVecSQLConfig: def do_request(url: str, api_key: str, request: TextToVecSQLRequest) -> TextToVecSQLResponse: """Do a request to the Text to Vector SQL server.""" try: + # 使用新的聊天完成 API 格式 response = requests.post( - url, json={"text_input": request.prompt}, headers={"Authorization": f"Bearer {api_key}"} + "https://cloud.infini-ai.com/AIStudio/inference/api/if-dce5zpkpwhejio5f/v1/chat/completions", + headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, + json={ + "model": "/mnt/DataFlow/ydw/model/UniVectorSQL-7B-LoRA-Step800", + "messages": [ + { + "role": "system", + "content": request.prompt, # 完整的系统提示词（包含所有 schema 和规则） + }, + { + "role": "user", + "content": request.natural_language_question, # 用户的自然语言问题 + }, + ], + "max_tokens": 2048, + "temperature": 0.05, # SQL 生成任务用较低的温度保证准确性 + "top_p": 0.95, + }, ) return TextToVecSQLResponse.handle_response(response) except Exception as e: - return TextToVecSQLResponse(sql="", error_message=str(e), error_code=response.status_code) + # print("[log] error: ", str(e)) + return TextToVecSQLResponse( + results={}, + error_message=str(e), + error_code=response.status_code if "response" in locals() else 500, + ) def get_vector_query(natural_language_question: str, table_schema: str) -> str: """Get a vector query from a natural language question and table schema. - IMPORTANT: Before calling this tool, you MUST translate the natural_language_question to English if it is not already in English. + IMPORTANT: Before calling this tool, you MUST translate the natural_language_question to English if it is not already in English. Find the column names that must be returned in natural_1anguage_question, and you also need to add prompts to inform the model of these column names that must be returned. This tool requires English input for optimal performance. Use this tool for natural language questions that require a vector query.